WO2018049858A1 - Calibration method for finger vein identification apparatus - Google Patents

Calibration method for finger vein identification apparatus Download PDF

Info

Publication number
WO2018049858A1
WO2018049858A1 PCT/CN2017/087841 CN2017087841W WO2018049858A1 WO 2018049858 A1 WO2018049858 A1 WO 2018049858A1 CN 2017087841 W CN2017087841 W CN 2017087841W WO 2018049858 A1 WO2018049858 A1 WO 2018049858A1
Authority
WO
WIPO (PCT)
Prior art keywords
finger vein
pixel
section
cross
images
Prior art date
Application number
PCT/CN2017/087841
Other languages
French (fr)
Chinese (zh)
Inventor
梁添才
刘建平
金晓峰
黎明
Original Assignee
广州广电运通金融电子股份有限公司
广州广电运通信息科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 广州广电运通金融电子股份有限公司, 广州广电运通信息科技有限公司 filed Critical 广州广电运通金融电子股份有限公司
Publication of WO2018049858A1 publication Critical patent/WO2018049858A1/en

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/22—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/14—Vascular patterns

Definitions

  • the present invention relates to the field of image acquisition, and in particular to a calibration method for a finger vein recognition device.
  • Vein recognition technology achieves the purpose of identification by in vivo recognition of vein images in the fingers or palms. It has high anti-counterfeiting, in vivo detection, high accuracy, adaptability and ease of use.
  • the finger vein recognition device involves a series of contents such as a light source, an optical lens, a photosensitive chip, etc.
  • the application area of the identification device covers various regions of China, and the external climate and temperature affect the luminous intensity of the near-infrared LED lamp of the device, and the optical lens transmittance At the same time, the external light directly interferes with the light received by the photosensitive chip, thereby affecting the acquired image, and finally results in a decrease in the recognition success rate of the finger vein recognition device.
  • the invention provides a calibration method for a finger vein recognition device, which can effectively reduce the influence of factors such as climate, temperature and illumination in different regions on the recognition success rate of the finger vein recognition device.
  • an embodiment of the present invention provides a calibration method for a finger vein recognition device, including the steps of:
  • ⁇ s is a preset standard value of the finger vein recognition device
  • ( ⁇ , ⁇ ) is a Gaussian distribution parameter obtained by the above step
  • sign is a symbol function
  • ⁇ is the infrared image.
  • the recognition algorithm of the finger vein recognition device provided by the invention directly recognizes on the collected image, and the consistency of the collected image directly affects the success rate of the device identification, and the above steps improve the collection of different devices in different environments.
  • the consistency of the image can effectively reduce the influence of image acquisition consistency on the recognition algorithm of the finger vein recognition device and improve the recognition success rate.
  • the Gaussian distribution parameter ( ⁇ , ⁇ ) of the gray value of the cross-section pixel of the collected N*L finger vein images is calculated by the following steps;
  • S21 Perform binarization on each of the finger vein images to divide a finger vein region, and obtain a finger vein line based on the finger vein region;
  • the gray level of the cross-section of each vein basically conforms to the Gaussian distribution, and the above steps are used to enrich the statistical data of each cross-section of each finger vein line, so that the statistical result is more accurate.
  • the invention also proposes a calibration method for another finger vein recognition device, comprising the steps of:
  • ⁇ s is a preset standard value of the finger vein recognition device
  • ( ⁇ r , ⁇ r ) is a Gaussian distribution parameter obtained by the above step
  • ⁇ is a gamma of the infrared image sensor Horse mapping coefficient
  • mapping coefficient ⁇ calculated the gamma adjusted values of the input pixel f and pixel output value f. 1 mapping relationship in the current state of the infrared image sensor is acquired, then the vein authentication device adjusted by said means The pixel output value f 1 is identified to obtain the recognition result.
  • the following steps and formulas may be used to calculate the Gaussian distribution parameter of the gray value of the cross-section pixel of each of the finger vein images collected:
  • M t is the number of pixels corresponding to the t-th cross-section, T ⁇ 3;
  • the following formula can be used to calculate a Gaussian distribution parameter ( ⁇ r , ⁇ r ) of the gray value of the cross-section pixel of all selected finger vein images that meet the requirements of the finger vein image width:
  • ⁇ r ( ⁇ 1 + ⁇ 2 + ⁇ 3 +...+ ⁇ p )/P
  • ⁇ 1 , ⁇ 2 , ⁇ 3 , ..., ⁇ p are respectively pixel gray values of the gray values of the cross-section pixels of each finger vein image selected Degree average.
  • the following formula can also be used to calculate the Gaussian distribution parameter ( ⁇ r , ⁇ r ) of the gray value of the cross-section pixel of all the finger vein images selected:
  • the NIBLACK image binarization method is used to binarize each of the finger vein images to extract a finger vein region; after the extracted finger vein region is segmented, skeleton extraction is performed. Finger vein line.
  • the calibration method of the finger vein recognition device of the present invention obtains the adjustment parameters of the infrared image sensor corresponding to the finger vein recognition device according to the gray scale change of the vein region of the finger vein acquisition image, which can effectively reduce the difference The effects of regional climate, temperature and light.
  • FIG. 1 is a schematic flow chart of a calibration method of a finger vein recognition device according to a first embodiment of the present invention.
  • FIG. 2 is a schematic diagram of a specific process of step S102 in FIG. 1.
  • Fig. 3 is a schematic view showing the vein extraction in the first embodiment of the present invention.
  • Fig. 4 is a schematic cross-sectional view showing a vein in the first embodiment of the present invention.
  • FIG. 5 is a schematic flow chart of a calibration method of a finger vein recognition device according to Embodiment 2 of the present invention.
  • FIG. 6 is a schematic diagram of a specific process of step S202 in FIG. 5.
  • FIG. 7 is a schematic flow chart of a calibration method of a finger vein recognition device according to a third embodiment of the present invention.
  • FIG. 8 is a schematic diagram of a specific process of step S302 in FIG. 7.
  • FIG. 9 is a schematic diagram of a specific process of step S304 in FIG. 7.
  • the invention provides a calibration method for a finger vein recognition device for performing corresponding calibration of a finger vein recognition device according to a current environment before using a finger vein recognition device.
  • the external environment including external climate, temperature and illumination
  • the present invention provides a calibration method for a finger vein recognition device, which is to improve the consistency of image collection under different environments in different finger vein recognition devices, thereby reducing the influence of the finger vein recognition device on environmental interference, thereby improving The recognition accuracy of the finger vein recognition device.
  • the calibration method of the finger vein recognition device of the present invention will be specifically described below by way of various embodiments.
  • the calibration method for a finger vein recognition device according to the first embodiment of the present invention includes steps S101-S104:
  • This step is used to acquire a finger vein image.
  • the infrared image sensor using the finger vein recognition device irradiates the fingers of the N collectors with the finger vein infrared light source of the preset intensity, and collects the L persons of the N state under the current state (in the same regional environment). Refers to the vein image to obtain N*L finger vein images, where N ⁇ 2, L ⁇ 1.
  • S102 Calculate a Gaussian distribution parameter ( ⁇ , ⁇ ) of the cross-sectional pixel gray value of the collected N*L finger vein images; wherein, the cross-sectional pixel gray value of each of the finger vein images conforms to a Gaussian distribution.
  • This step is used to count the gray values of the collected N*L finger vein images.
  • the step may be implemented by the following steps, including steps S1021-S1023, where:
  • S1021 Perform binarization on each of the finger vein images to divide a finger vein region, and obtain a finger vein line based on the finger vein region;
  • FIG. 3 is a schematic diagram of vein extraction in the first embodiment of the present invention, and the vein line 32 is extracted from the vein image 31.
  • each of the finger vein images is binarized by using a NIBLACK image binarization method. Thereby extracting the finger vein area. After the extracted finger vein region is divided, skeleton extraction is performed to obtain a finger vein line.
  • the gray value of the i-th pixel of the b-th cross-section, M b is the number of pixels corresponding to the b-th cross-section, T ⁇ 3; wherein the gray value of the cross-section pixel of each of the finger vein images conforms to Gaussian distributed;
  • FIG. 4 is a schematic cross-sectional view of a vein in the first embodiment of the present invention. Specifically, the extracted vein lines 40 are equally spaced T sections, and a vein section 41, a vein section 42, and a vein section 43 are obtained.
  • S103 Pass the formula: Calculating a gamma mapping coefficient of the infrared image sensor, wherein ( ⁇ s , ⁇ s ), ⁇ s is a preset standard value of the finger vein recognition device, and ( ⁇ , ⁇ ) is a Gaussian distribution parameter obtained by the above step , ⁇ is the gamma mapping coefficient of the infrared image sensor.
  • the gamma mapping coefficient GAMMA is used to correct the infrared image sensor in order to make the output images of the different devices, in different temperatures and regions, when the same finger is collected, as much as possible, thereby ensuring the device. Identify the effect.
  • the preset adjustment formula can be adopted.
  • the image pixels in the current state acquired by the finger vein recognition device through the infrared image sensor are calibrated, and the pixel-calibrated image is subjected to finger vein recognition to obtain a recognition result.
  • the algorithm process used for the identification may be in a manner well known to those skilled in the art, and the description is omitted here.
  • the infrared image sensor using the finger vein recognition device collects at least one finger vein image of each person and each finger, and takes multiple sections for each finger vein line, enriching the statistical data and making the statistical result more Accurately, at the same time, the correction of the infrared image sensor by the gamma mapping coefficient in the first embodiment enables different finger vein recognition devices to output the same finger as uniformly as possible when the same finger is collected under different temperatures and regions. The recognition success rate of the finger vein recognition device is improved.
  • the calibration method of the finger vein recognition device provided by the second embodiment of the present invention includes steps S201-S206:
  • S201 using an infrared image sensor of the finger vein recognition device to collect L finger images of N individuals in the current state, wherein N ⁇ 2, L ⁇ 1;
  • This step is used to acquire a finger vein image.
  • the infrared image sensor using the finger vein recognition device irradiates the fingers of the N collectors with the finger vein infrared light source of the preset intensity, and collects the L persons of the N state under the current state (in the same regional environment). Refers to the vein image to obtain N*L finger vein images, where N ⁇ 2, L ⁇ 1.
  • S202 Calculate a Gaussian distribution parameter of the gray value of the cross-section pixel of each of the finger vein images collected respectively; wherein the gray value of the cross-section pixel of each of the finger vein images conforms to a Gaussian distribution, and the Gaussian distribution parameter includes Pixel gray mean and standard deviation;
  • This step is used to count the gray values of the collected N*L finger vein images.
  • the step may be implemented by the following steps, including steps S2021-S2023, where:
  • S2021 Perform binarization on each of the finger vein images to divide a finger vein region, and obtain a finger vein line based on the finger vein region;
  • each of the finger vein images is binarized by using a NIBLACK image binarization method to extract a finger vein region. After the extracted finger vein region is divided, skeleton extraction is performed to obtain a finger vein line.
  • S2022 taking equal intervals of T sections for each of the finger vein lines, taking the gray value of all the pixels of each section, and recording among them
  • the gray value of the i-th pixel of the t-th cross-section, M t is the number of pixels corresponding to the t-th cross-section, T ⁇ 3; wherein the gray value of the cross-section pixel of each of the finger vein images conforms to Gauss distributed.
  • S203 Calculate a width of each of the finger vein images according to a Gaussian distribution parameter of a cross-sectional pixel gray value of each of the finger vein images, and then calculate an average value h w of widths of all the finger vein images, and select the All finger vein images having a width between h w (1 ⁇ A%) in N*L finger vein images; wherein 0 ⁇ A ⁇ 50, the width of each of the finger vein images refers to each of the fingers The number of pixels of the cross-section pixel gray value on the vein image is smaller than the corresponding pixel gray mean value;
  • This step is used to count the gray value of the selected finger vein image.
  • the Gaussian distribution parameter ( ⁇ r , ⁇ r ) of the gray value of the cross-section pixel of all the finger vein images selected is calculated using the following formula:
  • ⁇ r ( ⁇ 1 + ⁇ 2 + ⁇ 3 +...+ ⁇ p )/P
  • ⁇ 1 , ⁇ 2 , ⁇ 3 , ..., ⁇ p are respectively pixel gray values of the gray values of the cross-section pixels of each finger vein image selected Degree average.
  • S206 Pass the formula according to the calculated gamma mapping coefficient ⁇ Adjusting the mapping relationship between the pixel input value f and the pixel output value f 1 in the current state acquired by the infrared image sensor, and then identifying the adjusted pixel output value f 1 by the finger vein recognition device to obtain the recognition result.
  • the preset adjustment formula can be adopted.
  • the image pixels in the current state acquired by the finger vein recognition device through the infrared image sensor are calibrated, and the pixel-calibrated image is subjected to finger vein recognition to obtain a recognition result.
  • the finger vein image is selected when calculating the Gaussian distribution parameter of the gray value of the cross-section pixel of the finger vein image, and it is preferable to select all the finger vein images collected.
  • the experimental result is more stable.
  • the width of the finger vein image is 0.9 to 1.1 times the average width of the finger vein image, and the gamma mapping coefficient is obtained according to the Gaussian distribution parameter of the gray value of the cross-section pixel of the selected finger vein image, and the gamma is used.
  • the recognition success rate of the finger vein identification device after the mapping coefficient calibration is higher.
  • FIG. 7 is a schematic flowchart of a method for calibrating a finger vein recognition device according to Embodiment 3 of the present invention.
  • the calibration method for a finger vein recognition device according to Embodiment 3 of the present invention includes steps S301-S306:
  • the infrared image sensor of the finger vein recognition device is used to collect the image of each finger vein of the N person in the current state, wherein N ⁇ 2, L ⁇ 1;
  • This step is used to acquire a finger vein image.
  • the infrared image sensor using the finger vein recognition device irradiates the fingers of the N collectors with the finger vein infrared light source of the preset intensity, and collects the L persons of the N state under the current state (in the same regional environment). Refers to the vein image to obtain N*L finger vein images, where N ⁇ 2, L ⁇ 1.
  • S302 Calculate a Gaussian distribution parameter of the gray value of the cross-section pixel of each of the finger vein images collected, wherein the gray value of the cross-section pixel of each of the finger vein images conforms to a Gaussian distribution, and the Gaussian distribution parameter includes Pixel gray mean and standard deviation;
  • This step is used to count the gray values of the collected N*L finger vein images.
  • the step may be implemented by the following steps, including steps S3021-S3023, where:
  • S3021 Perform binarization on each of the finger vein images to divide a finger vein region, and obtain a finger vein line based on the finger vein region;
  • each of the finger vein images is binarized by using a NIBLACK image binarization method to extract a finger vein region. After the extracted finger vein region is divided, skeleton extraction is performed to obtain a finger vein line.
  • S3022 Taking equal intervals of T sections for each of the finger vein lines, taking the gray value of all the pixels of each section, and recording among them
  • the gray value of the i-th pixel of the t-th cross-section, M t is the number of pixels corresponding to the t-th cross-section, T ⁇ 3, wherein the gray value of the cross-section pixel of each of the finger vein images conforms to Gaussian distributed.
  • S303 Calculate a width of each of the finger vein images according to a Gaussian distribution parameter of a gray value of a cross-sectional pixel of each of the finger vein images, and then calculate an average value h w of widths of all the finger vein images, and select the All finger vein images having a width between h w (1 ⁇ A%) in N*L finger vein images; wherein 0 ⁇ A ⁇ 50, the width of each of the finger vein images refers to each of the fingers
  • the grayscale value of the cross-section pixel on the vein image is smaller than the number of pixels of the corresponding grayscale mean of the pixel.
  • This step is used to count the gray value of the selected finger vein image.
  • the step can be as follows:
  • the implementation includes steps S3041-S3042, wherein:
  • S305 Pass the formula: Calculating a gamma mapping coefficient of the infrared image sensor, wherein ( ⁇ s , ⁇ s ), ⁇ s is a preset standard value of the finger vein recognition device, and ( ⁇ r , ⁇ r ) is calculated by the above step S304
  • the cross-sectional pixel gray value Gaussian distribution parameter of the selected finger vein image, ⁇ is the gamma mapping coefficient of the infrared image sensor.
  • the preset adjustment formula can be adopted.
  • the image pixels in the current state acquired by the finger vein recognition device through the infrared image sensor are calibrated, and the pixel-calibrated image is subjected to finger vein recognition to obtain a recognition result.
  • the finger vein image is selected when calculating the Gaussian distribution parameter of the gray value of the cross-section pixel of the finger vein image, and it is preferable to select all the finger vein images collected.
  • the experimental result is more stable.
  • the width of the finger vein image is 0.9 to 1.1 times the average width of the finger vein image, and the gamma mapping coefficient is obtained according to the Gaussian distribution parameter of the gray value of the cross-section pixel of the selected finger vein image, and the gamma is used.
  • the recognition success rate of the finger vein identification device after the mapping coefficient calibration is higher.
  • the difference from the second embodiment is that the method for calculating the Gaussian distribution parameter of the cross-sectional pixel gray value of the selected finger vein image is different.
  • the second embodiment is to first find the interface pixel gray scale of each selected finger vein image.
  • the Gaussian distribution parameter is calculated, and the Gaussian distribution parameters of all the finger vein images are calculated.
  • the Gaussian distribution parameters of all the gray values of the finger vein segments are directly obtained.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Human Computer Interaction (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
  • Image Input (AREA)

Abstract

Disclosed is a calibration method for a finger vein identification apparatus. The method comprises the following steps: using an infrared image sensor of a finger vein identification apparatus to collect L respective finger vein images of N persons in a current state, wherein N≥2 and L≥1 (S101); calculating Gaussian distribution parameters of section pixel grayscale values of the N*L finger vein images, wherein the section pixel grayscale values of each of the finger vein images comply with a Gaussian distribution (S102); by means of formula (1), calculating a Gamma mapping coefficient of the infrared image sensor, wherein (μs, σs) and γs are pre-set standard values of the finger vein identification apparatus; (μ, σ) is a Gaussian distribution parameter obtained in the previous steps; and γ is a Gamma mapping coefficient of the infrared image sensor (S103); and according to the Gamma mapping coefficient obtained by means of calculation, adjusting a mapping relationship between a pixel input value f and a pixel output value f1 obtained by the infrared image sensor in the current state, and the finger vein identification apparatus then identifying the adjusted pixel output value f1, so as to obtain an identification result (S104). The calibration method can effectively reduce the influence of factors, such as the climate, temperature and illumination of different regions, on the identification success rate of the finger vein identification apparatus.

Description

一种指静脉识别装置的校准方法Calibration method for finger vein recognition device 技术领域Technical field
本发明涉及图像采集领域，尤其涉及一种指静脉识别装置的校准方法。The present invention relates to the field of image acquisition, and in particular to a calibration method for a finger vein recognition device.
背景技术Background technique
随着时代的发展，个人信息安全越来越重要。选择合理的认证技术是保证信息安全的必要因素。传统的认证技术是基于个人密码，而密码被破解的概率越来越高。生物认证将成为今后几年信息产业的重要变革，越来越多的个人、消费者、公司乃至政府机构都承认，现有的基于智能卡、身份证号和密码的身份识别***是远远不够的，生物特征识别技术将在未来提供安全认证方面占据重要的地位。With the development of the times, personal information security is becoming more and more important. Choosing a reasonable authentication technology is a necessary factor to ensure information security. Traditional authentication techniques are based on personal passwords, and the probability of passwords being cracked is increasing. Bio-certification will become an important change in the information industry in the next few years. More and more individuals, consumers, companies and even government agencies recognize that existing identification systems based on smart cards, ID numbers and passwords are not enough. Biometrics technology will play an important role in providing security certification in the future.
静脉识别技术是通过对手指或手掌中静脉图像进行活体识别来达到认证目的，具有高度防伪、活体检测、高度准确、适应性强和简便易用的特性。Vein recognition technology achieves the purpose of identification by in vivo recognition of vein images in the fingers or palms. It has high anti-counterfeiting, in vivo detection, high accuracy, adaptability and ease of use.
指静脉识别装置涉及光源、光学镜头、光敏芯片等一系列内容，该识别装置的应用区域覆盖我国的各个地域，外界的气候和温度会影响设备近红外LED灯的发光强度，光学镜头透光率，同时外部光照会直接干扰光敏芯片接收到的光线，进而影响到采集到的图像，最终导致指静脉识别装置的识别成功率的降低。为了有效的提高指静脉识别装置的识别成功率，需要在使用前对指静脉识别装置进行相应校准。The finger vein recognition device involves a series of contents such as a light source, an optical lens, a photosensitive chip, etc. The application area of the identification device covers various regions of China, and the external climate and temperature affect the luminous intensity of the near-infrared LED lamp of the device, and the optical lens transmittance At the same time, the external light directly interferes with the light received by the photosensitive chip, thereby affecting the acquired image, and finally results in a decrease in the recognition success rate of the finger vein recognition device. In order to effectively improve the recognition success rate of the finger vein recognition device, it is necessary to perform corresponding calibration on the finger vein recognition device before use.
发明内容Summary of the invention
本发明提出了一种指静脉识别装置的校准方法，能够有效的降低不同地域气候、温度和光照等因素对指静脉识别装置的识别成功率的影响。The invention provides a calibration method for a finger vein recognition device, which can effectively reduce the influence of factors such as climate, temperature and illumination in different regions on the recognition success rate of the finger vein recognition device.
为实现上述目的，本发明实施例提出了一种指静脉识别装置的校准方法，包括步骤：To achieve the above object, an embodiment of the present invention provides a calibration method for a finger vein recognition device, including the steps of:
S1、利用指静脉识别装置的红外图像传感器采集当前状态下的N个人各L幅指静脉图像，其中N≥2，L≥1；S1, using an infrared image sensor of the finger vein recognition device to collect L finger images of N individuals in the current state, wherein N≥2, L≥1;
S2、计算采集到的所述N*L个指静脉图像的截面像素灰度值的高斯分布参数(μ，σ)；其中，μ为均值，σ为标准差，每一所述指静脉图像的截面像素灰度值符合高斯分布；S2. Calculating a Gaussian distribution parameter (μ, σ) of the gray value of the cross-section pixel of the collected N*L finger vein images; wherein μ is a mean value, and σ is a standard deviation, and each of the finger vein images is The gray value of the cross-section pixel conforms to the Gaussian distribution;
S3、通过以下公式计算所述红外图像传感器的伽马映射系数：S3. Calculate a gamma mapping coefficient of the infrared image sensor by using the following formula:
Figure PCTCN2017087841-appb-000001
Figure PCTCN2017087841-appb-000001
其中，(μs，σs)、γs为所述指静脉识别装置预设的标准值，(μ，σ)为上述步骤得到的高斯分布参数，sign为符号函数，γ为所述红外图像传感器的伽马映射系数； Wherein (μ s , σ s ), γ s is a preset standard value of the finger vein recognition device, (μ, σ) is a Gaussian distribution parameter obtained by the above step, sign is a symbol function, and γ is the infrared image. Gamma mapping coefficient of the sensor;
S4、根据计算得到的所述伽马映射系数γ，调整所述红外图像传感器获取的当前状态下的像素输入值f和像素输出值f1映射关系，然后通过所述指静脉识别装置对调整后的像素输出值f1进行识别以获取识别结果。S4, the map of the gamma coefficient γ calculated, adjusting f. 1 mapping between the input pixel f and pixel values of the current state of the output values of the infrared image sensor is acquired, then the vein authentication device adjusted by said means The pixel output value f 1 is identified to obtain the recognition result.
本发明提供的指静脉识别装置的识别算法是直接在采集到的图像上进行识别，采集到的图像的一致性会直接影响到装置识别成功率，通过上述步骤提高了不同设备在不同环境下采集图像的一致性，能有效的减小图像采集一致性对指静脉识别装置的识别算法的影响，提高识别成功率。The recognition algorithm of the finger vein recognition device provided by the invention directly recognizes on the collected image, and the consistency of the collected image directly affects the success rate of the device identification, and the above steps improve the collection of different devices in different environments. The consistency of the image can effectively reduce the influence of image acquisition consistency on the recognition algorithm of the finger vein recognition device and improve the recognition success rate.
作为上述方案的改进，通过以下步骤计算采集到的所述N*L个指静脉图像的截面像素灰度值的高斯分布参数(μ，σ)；As a modification of the above solution, the Gaussian distribution parameter (μ, σ) of the gray value of the cross-section pixel of the collected N*L finger vein images is calculated by the following steps;
S21、对每一所述指静脉图像进行二值化以划分出指静脉区域，基于所述指静脉区域得到指静脉线条；S21: Perform binarization on each of the finger vein images to divide a finger vein region, and obtain a finger vein line based on the finger vein region;
S22、对每一所述指静脉线条取等间距的T个截面，共得到B＝N*L*T个截面；取每个截面所有像素的灰度值，记为
Figure PCTCN2017087841-appb-000002
其中
Figure PCTCN2017087841-appb-000003
为第b个截面的第i个像素的灰度值，Mb为第b个截面所对应的像素个数，T≥3；S22, taking T sections of equal intervals for each of the finger vein lines, and obtaining a total of B=N*L*T sections; taking the gray value of all the pixels of each section,
Figure PCTCN2017087841-appb-000002
among them
Figure PCTCN2017087841-appb-000003
Is the gray value of the i-th pixel of the b-th cross-section, and M b is the number of pixels corresponding to the b-th cross-section, T≥3;
S23、通过以下公式计算得到所述B个截面像素灰度值的高斯分布参数(μ，σ)：S23. Calculate a Gaussian distribution parameter (μ, σ) of the gray values of the B cross-section pixels by using the following formula:
Figure PCTCN2017087841-appb-000004
Figure PCTCN2017087841-appb-000004
Figure PCTCN2017087841-appb-000005
Figure PCTCN2017087841-appb-000005
作为上述方案的改进，通过公式
Figure PCTCN2017087841-appb-000006
调整所述红外图像传感器获取的当前状态下的像素输入值f和像素输出值f1映射关系。As an improvement of the above scheme, through the formula
Figure PCTCN2017087841-appb-000006
Adjusting a mapping relationship between the pixel input value f and the pixel output value f 1 in the current state acquired by the infrared image sensor.
根据实验数据统计可知静脉图像中，每根静脉的截面像素灰度基本符合高斯分布，通过上述步骤对每根指静脉线条取多个截面丰富了统计数据，使统计结果更精确。According to the experimental data, it can be seen that in the vein image, the gray level of the cross-section of each vein basically conforms to the Gaussian distribution, and the above steps are used to enrich the statistical data of each cross-section of each finger vein line, so that the statistical result is more accurate.
本发明还提出了另一种指静脉识别装置的校准方法，包括步骤：The invention also proposes a calibration method for another finger vein recognition device, comprising the steps of:
S1、利用指静脉识别装置的红外图像传感器采集当前状态下的N个人各L幅指静脉图像，其中N≥2，L≥1；S1, using an infrared image sensor of the finger vein recognition device to collect L finger images of N individuals in the current state, wherein N≥2, L≥1;
S2、分别计算采集到的每一所述指静脉图像的截面像素灰度值的高斯分布参数；其中，每一所述指静脉图像的截面像素灰度值符合高斯分布，所述高斯分布参数包括像素灰度均值和标准差；S2, respectively calculating a Gaussian distribution parameter of the gray value of the cross-section pixel of each of the finger vein images collected; wherein, the gray value of the cross-section pixel of each of the finger vein images conforms to a Gaussian distribution, and the Gaussian distribution parameter includes Pixel gray mean and standard deviation;
S3、根据每一所述指静脉图像的截面像素灰度值的高斯分布参数计算得到每一所述指静脉图像的 宽度，然后计算所有指静脉图像的宽度的平均值hw，并选取所述N*L个指静脉图像中宽度在hw(1±A％)之间的所有指静脉图像；其中，0＜A≤50，每一所述指静脉图像的宽度是指每一所述指静脉图像上的截面像素灰度值小于其对应的像素灰度均值的像素个数；S3. Calculate a width of each of the finger vein images according to a Gaussian distribution parameter of a gray value of a cross-sectional pixel of each of the finger vein images, and then calculate an average value h w of widths of all the finger vein images, and select the All finger vein images having a width between h w (1±A%) in N*L finger vein images; wherein 0<A≤50, the width of each of the finger vein images refers to each of the fingers The number of pixels of the cross-section pixel gray value on the vein image is smaller than the corresponding pixel gray mean value;
S4、计算所选取的所有指静脉图像的截面像素灰度值的高斯分布参数(μr，σr)；S4. Calculate a Gaussian distribution parameter (μ r , σ r ) of the gray value of the cross-section pixel of all the finger vein images selected;
S5、通过以下公式计算所述红外图像传感器的伽马映射系数：S5. Calculate a gamma mapping coefficient of the infrared image sensor by using the following formula:
Figure PCTCN2017087841-appb-000007
Figure PCTCN2017087841-appb-000007
其中，(μs，σs)、γs为所述指静脉识别装置预设的标准值，(μr，σr)为上述步骤得到的高斯分布参数，γ为所述红外图像传感器的伽马映射系数；Wherein (μ s , σ s ), γ s is a preset standard value of the finger vein recognition device, (μ r , σ r ) is a Gaussian distribution parameter obtained by the above step, and γ is a gamma of the infrared image sensor Horse mapping coefficient;
S6、根据计算得到的所述伽马映射系数γ，调整所述红外图像传感器获取的当前状态下的像素输入值f和像素输出值f1映射关系，然后通过所述指静脉识别装置对调整后的像素输出值f1进行识别以获取识别结果。S6, according to the mapping coefficient γ calculated the gamma adjusted values of the input pixel f and pixel output value f. 1 mapping relationship in the current state of the infrared image sensor is acquired, then the vein authentication device adjusted by said means The pixel output value f 1 is identified to obtain the recognition result.
作为上述方案的改进，可采用以下步骤和公式来计算采集到的每一所述指静脉图像的截面像素灰度值的高斯分布参数：As an improvement of the above solution, the following steps and formulas may be used to calculate the Gaussian distribution parameter of the gray value of the cross-section pixel of each of the finger vein images collected:
对每一所述指静脉图像进行二值化以划分出指静脉区域，基于所述指静脉区域得到指静脉线条；Dividing each of the finger vein images to divide a finger vein region, and obtaining a finger vein line based on the finger vein region;
对每一所述指静脉线条取等间距的T个截面，取每个截面所有像素的灰度值，记为
Figure PCTCN2017087841-appb-000008
其中
Figure PCTCN2017087841-appb-000009
为第t个截面的第i个像素的灰度值，Mt为第t个截面所对应的像素个数，T≥3；Taking equal intervals of T sections for each of the finger vein lines, taking the gray value of all pixels of each section, recorded as
Figure PCTCN2017087841-appb-000008
among them
Figure PCTCN2017087841-appb-000009
The gray value of the i-th pixel of the t-th cross-section, M t is the number of pixels corresponding to the t-th cross-section, T≥3;
Figure PCTCN2017087841-appb-000010
Figure PCTCN2017087841-appb-000010
Figure PCTCN2017087841-appb-000011
Figure PCTCN2017087841-appb-000011
作为上述方案的改进，可采用以下公式来计算符合指静脉图像宽度要求的所选取的所有指静脉图像的截面像素灰度值的高斯分布参数(μr，σr)：As an improvement of the above scheme, the following formula can be used to calculate a Gaussian distribution parameter (μ r , σ r ) of the gray value of the cross-section pixel of all selected finger vein images that meet the requirements of the finger vein image width:
μr＝(μ1+μ2+μ3+...+μp)/Pμ r =(μ 1 +μ 2 +μ 3 +...+μ p )/P
Figure PCTCN2017087841-appb-000012
Figure PCTCN2017087841-appb-000012
其中，P为所选取的所有指静脉图像的总个数，μ1，μ2，μ3，...，μp分别为所选取的每一指静脉图像的截面像素灰度值的像素灰度均值。Where P is the total number of all finger vein images selected, μ 1 , μ 2 , μ 3 , ..., μ p are respectively pixel gray values of the gray values of the cross-section pixels of each finger vein image selected Degree average.
作为上述方案的改进，还可采用以下公式来计算所选取的所有指静脉图像的截面像素灰度值的高斯分布参数(μr，σr)：As an improvement of the above scheme, the following formula can also be used to calculate the Gaussian distribution parameter (μ r , σ r ) of the gray value of the cross-section pixel of all the finger vein images selected:
Figure PCTCN2017087841-appb-000013
Figure PCTCN2017087841-appb-000013
Figure PCTCN2017087841-appb-000014
Figure PCTCN2017087841-appb-000014
对每一所述指静脉线条取等间距的T2个截面，共得到B2＝P*T2个截面；取每个截面所有像素的灰度值，记为
Figure PCTCN2017087841-appb-000015
其中
Figure PCTCN2017087841-appb-000016
为第b2个截面的第i个像素的灰度值，Mb2为第b2个截面所对应的像素个数，P为所选取的所有指静脉图像的总个数，T2≥3。Taking T 2 sections of equal spacing for each of the finger vein lines, a total of B 2 =P*T 2 sections are obtained; the gray values of all the pixels of each section are recorded as
Figure PCTCN2017087841-appb-000015
among them
Figure PCTCN2017087841-appb-000016
2 pixels of gray value of the i-th cross-section b, M b2 B is the number of pixels corresponding to the two-section, refers to the total number of all the vein image P is selected, T 2 ≥3.
作为上述方案的改进，采用NIBLACK图像二值化方法对每一所述指静脉图像进行二值化处理，从而提取指静脉区域；对提取的所述指静脉区域进行分割后，进行骨架提取，得到指静脉线条。As an improvement of the above solution, the NIBLACK image binarization method is used to binarize each of the finger vein images to extract a finger vein region; after the extracted finger vein region is segmented, skeleton extraction is performed. Finger vein line.
作为上述方案的改进，A＝10。As an improvement of the above scheme, A = 10.
作为上述方案的改进，通过公式
Figure PCTCN2017087841-appb-000017
调整所述红外图像传感器获取的当前状态下的像素输入值f和像素输出值f1映射关系。As an improvement of the above scheme, through the formula
Figure PCTCN2017087841-appb-000017
Adjusting a mapping relationship between the pixel input value f and the pixel output value f 1 in the current state acquired by the infrared image sensor.
综上所述，本发明所述指静脉识别装置的校准方法，根据指静脉采集图像的静脉区域的灰度变化，得到该指静脉识别装置对应的红外图像传感器的调整参数，可有效的降低不同地域气候、温度和光照等因素的影响。In summary, the calibration method of the finger vein recognition device of the present invention obtains the adjustment parameters of the infrared image sensor corresponding to the finger vein recognition device according to the gray scale change of the vein region of the finger vein acquisition image, which can effectively reduce the difference The effects of regional climate, temperature and light.
附图说明DRAWINGS
图1是本发明实施例一中一种指静脉识别装置的校准方法的流程示意图。1 is a schematic flow chart of a calibration method of a finger vein recognition device according to a first embodiment of the present invention.
图2是图1中步骤S102的具体流程示意图。FIG. 2 is a schematic diagram of a specific process of step S102 in FIG. 1.
图3是本发明实施例一中静脉提取示意图。Fig. 3 is a schematic view showing the vein extraction in the first embodiment of the present invention.
图4是本发明实施例一中静脉截面示意图。Fig. 4 is a schematic cross-sectional view showing a vein in the first embodiment of the present invention.
图5是本发明实施例二中一种指静脉识别装置的校准方法的流程示意图。 FIG. 5 is a schematic flow chart of a calibration method of a finger vein recognition device according to Embodiment 2 of the present invention.
图6是图5中步骤S202的具体流程示意图。FIG. 6 is a schematic diagram of a specific process of step S202 in FIG. 5.
图7是本发明实施例三中一种指静脉识别装置的校准方法的流程示意图。7 is a schematic flow chart of a calibration method of a finger vein recognition device according to a third embodiment of the present invention.
图8是图7中步骤S302的具体流程示意图。FIG. 8 is a schematic diagram of a specific process of step S302 in FIG. 7.
图9是图7中步骤S304的具体流程示意图。FIG. 9 is a schematic diagram of a specific process of step S304 in FIG. 7.
具体实施方式detailed description
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The technical solutions in the embodiments of the present invention are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present invention. It is obvious that the described embodiments are only a part of the embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts are within the scope of the present invention.
本发明提供一种指静脉识别装置的校准方法，用于在使用指静脉识别装置前根据当前环境对指静脉识别装置进行相应校准。如前所述，外界环境(包括外界的气候、温度和光照等因素)会影响到红外图像传感器所采集的图像，进而影响到指静脉识别装置的识别准确率。因此，本发明提供一种指静脉识别装置的校准方法，是为了提高不同的指静脉识别装置，在不同的环境下图像采集的一致性，从而减少指静脉识别装置受环境干扰的影响，从而提高指静脉识别装置的识别准确率。下面通过多个实施例对本发明的指静脉识别装置的校准方法进行具体描述。The invention provides a calibration method for a finger vein recognition device for performing corresponding calibration of a finger vein recognition device according to a current environment before using a finger vein recognition device. As mentioned above, the external environment (including external climate, temperature and illumination) affects the image acquired by the infrared image sensor, which in turn affects the recognition accuracy of the finger vein recognition device. Therefore, the present invention provides a calibration method for a finger vein recognition device, which is to improve the consistency of image collection under different environments in different finger vein recognition devices, thereby reducing the influence of the finger vein recognition device on environmental interference, thereby improving The recognition accuracy of the finger vein recognition device. The calibration method of the finger vein recognition device of the present invention will be specifically described below by way of various embodiments.
参见图1，是本发明实施例一提供的一种指静脉识别装置的校准方法的流程示意图，本发明实施例一提供的一种指静脉识别装置的校准方法包括步骤S101-S104：1 is a schematic flowchart of a method for calibrating a finger vein recognition device according to a first embodiment of the present invention. The calibration method for a finger vein recognition device according to the first embodiment of the present invention includes steps S101-S104:
S101、利用指静脉识别装置的红外图像传感器采集当前状态下的N个人各L幅指静脉图像，其中N≥2，L≥1；S101, using an infrared image sensor of the finger vein recognition device to collect L finger images of N individuals in the current state, wherein N≥2, L≥1;
该步骤用于采集指静脉图像。具体的，在该步骤中，利用指静脉识别装置的红外图像传感器采用预设强度的指静脉红外光源照射N个采集者的手指，采集当前状态(在同一区域环境)下的N个人各L幅指静脉图像，从而得到N*L个指静脉图像，其中N≥2，L≥1。This step is used to acquire a finger vein image. Specifically, in this step, the infrared image sensor using the finger vein recognition device irradiates the fingers of the N collectors with the finger vein infrared light source of the preset intensity, and collects the L persons of the N state under the current state (in the same regional environment). Refers to the vein image to obtain N*L finger vein images, where N≥2, L≥1.
S102：计算采集到的所述N*L个指静脉图像的截面像素灰度值的高斯分布参数(μ，σ)；其中，每一所述指静脉图像的截面像素灰度值符合高斯分布。S102: Calculate a Gaussian distribution parameter (μ, σ) of the cross-sectional pixel gray value of the collected N*L finger vein images; wherein, the cross-sectional pixel gray value of each of the finger vein images conforms to a Gaussian distribution.
该步骤用于对采集到的N*L个指静脉图像的灰度值进行统计。具体的，参考图2，该步骤可以通过以下步骤实现，包括步骤S1021-S1023，其中：This step is used to count the gray values of the collected N*L finger vein images. Specifically, referring to FIG. 2, the step may be implemented by the following steps, including steps S1021-S1023, where:
S1021：对每一所述指静脉图像进行二值化以划分出指静脉区域，基于所述指静脉区域得到指静脉线条；S1021: Perform binarization on each of the finger vein images to divide a finger vein region, and obtain a finger vein line based on the finger vein region;
参见图3，是本发明实施例一中静脉提取示意图，从静脉图像31中提取到静脉线条32，具体的，采用NIBLACK图像二值化方法对每一所述指静脉图像进行二值化处理，从而提取指静脉区域。对提取的所述指静脉区域进行分割后，进行骨架提取，从而得到指静脉线条。3 is a schematic diagram of vein extraction in the first embodiment of the present invention, and the vein line 32 is extracted from the vein image 31. Specifically, each of the finger vein images is binarized by using a NIBLACK image binarization method. Thereby extracting the finger vein area. After the extracted finger vein region is divided, skeleton extraction is performed to obtain a finger vein line.
S1022：对每一所述指静脉线条取等间距的T个截面，共得到B＝N*L*T个截面；取每个截面所有像素的灰度值，记为
Figure PCTCN2017087841-appb-000018
其中
Figure PCTCN2017087841-appb-000019
为第b个截面的第i个像素的灰度值，Mb为第b 个截面所对应的像素个数，T≥3；其中，每一所述指静脉图像的截面像素灰度值符合高斯分布；S1022: taking an equal interval of T sections for each of the finger vein lines, and obtaining a total of B=N*L*T sections; taking the gray value of all the pixels of each section, and recording
Figure PCTCN2017087841-appb-000018
among them
Figure PCTCN2017087841-appb-000019
The gray value of the i-th pixel of the b-th cross-section, M b is the number of pixels corresponding to the b-th cross-section, T≥3; wherein the gray value of the cross-section pixel of each of the finger vein images conforms to Gaussian distributed;
参见图4，是本发明实施例一中静脉截面示意图，具体的，对提取到的静脉线条40取等间距的T个截面，得到静脉截面41、静脉截面42、静脉截面43。4 is a schematic cross-sectional view of a vein in the first embodiment of the present invention. Specifically, the extracted vein lines 40 are equally spaced T sections, and a vein section 41, a vein section 42, and a vein section 43 are obtained.
S1023：通过以下公式计算得到所述B个截面像素灰度值的高斯分布参数(μ，σ)：S1023: Calculating a Gaussian distribution parameter (μ, σ) of the gray values of the B cross-section pixels by the following formula:
Figure PCTCN2017087841-appb-000020
Figure PCTCN2017087841-appb-000020
Figure PCTCN2017087841-appb-000021
Figure PCTCN2017087841-appb-000021
S103：通过公式：
Figure PCTCN2017087841-appb-000022
计算所述红外图像传感器的伽马映射系数，其中，(μs，σs)、γs为所述指静脉识别装置预设的标准值，(μ，σ)为上述步骤得到的高斯分布参数，γ为所述红外图像传感器的伽马映射系数。S103: Pass the formula:
Figure PCTCN2017087841-appb-000022
Calculating a gamma mapping coefficient of the infrared image sensor, wherein (μ s , σ s ), γ s is a preset standard value of the finger vein recognition device, and (μ, σ) is a Gaussian distribution parameter obtained by the above step , γ is the gamma mapping coefficient of the infrared image sensor.
本实施例通过伽马映射系数GAMMA对红外图像传感器进行校正的的作用是为了使得不同设备、在不同温度、区域等情况下，在采集同一个手指时，输出的图像尽可能一致，从而保证设备识别效果。In this embodiment, the gamma mapping coefficient GAMMA is used to correct the infrared image sensor in order to make the output images of the different devices, in different temperatures and regions, when the same finger is collected, as much as possible, thereby ensuring the device. Identify the effect.
根据计算得到的所述伽马映射系数γ，通过公式
Figure PCTCN2017087841-appb-000023
调整所述红外图像传感器获取的当前状态下的像素输入值f和像素输出值f1映射关系，然后通过所述指静脉识别装置对调整后的像素输出值f1进行识别以获取识别结果。Calculating the obtained gamma mapping coefficient γ according to the formula
Figure PCTCN2017087841-appb-000023
Adjusting the mapping relationship between the pixel input value f and the pixel output value f 1 in the current state acquired by the infrared image sensor, and then identifying the adjusted pixel output value f 1 by the finger vein recognition device to obtain the recognition result.
在该步骤中，根据步骤S103计算得到的伽马映射系数γ，即可通过预设的调整公式
Figure PCTCN2017087841-appb-000024
对指静脉识别装置通过红外图像传感器获取的当前状态下的图像像素进行校准，并将经过像素校准后的图像才进行指静脉识别，以得到识别结果。其中，识别所采用的算法过程可采用本领域技术人员公知的方式，在此省略描述。In this step, according to the gamma mapping coefficient γ calculated in step S103, the preset adjustment formula can be adopted.
Figure PCTCN2017087841-appb-000024
The image pixels in the current state acquired by the finger vein recognition device through the infrared image sensor are calibrated, and the pixel-calibrated image is subjected to finger vein recognition to obtain a recognition result. The algorithm process used for the identification may be in a manner well known to those skilled in the art, and the description is omitted here.
如上所述实施例一，使用指静脉识别装置的红外图像传感器采集多人、每人至少一幅指静脉图像，并对每根指静脉线条取多个截面，丰富了统计数据，使统计结果更精确，同时，通过实施例一中伽马映射系数对红外图像传感器的校正，使得不同指静脉识别装置、在不同温度、区域等情况下，在采集同一个手指时，输出的图像尽可能一致，提高了指静脉识别装置的识别成功率。 In the first embodiment, the infrared image sensor using the finger vein recognition device collects at least one finger vein image of each person and each finger, and takes multiple sections for each finger vein line, enriching the statistical data and making the statistical result more Accurately, at the same time, the correction of the infrared image sensor by the gamma mapping coefficient in the first embodiment enables different finger vein recognition devices to output the same finger as uniformly as possible when the same finger is collected under different temperatures and regions. The recognition success rate of the finger vein recognition device is improved.
参见图5，是本发明实施例二提供的一种指静脉识别装置的校准方法的流程示意图，本发明实施例二提供的一种指静脉识别装置的校准方法包括步骤S201-S206：5 is a schematic flowchart of a calibration method of a finger vein recognition device according to a second embodiment of the present invention. The calibration method of the finger vein recognition device provided by the second embodiment of the present invention includes steps S201-S206:
S201：利用指静脉识别装置的红外图像传感器采集当前状态下的N个人各L幅指静脉图像，其中N≥2，L≥1；S201: using an infrared image sensor of the finger vein recognition device to collect L finger images of N individuals in the current state, wherein N≥2, L≥1;
该步骤用于采集指静脉图像。具体的，在该步骤中，利用指静脉识别装置的红外图像传感器采用预设强度的指静脉红外光源照射N个采集者的手指，采集当前状态(在同一区域环境)下的N个人各L幅指静脉图像，从而得到N*L个指静脉图像，其中N≥2，L≥1。This step is used to acquire a finger vein image. Specifically, in this step, the infrared image sensor using the finger vein recognition device irradiates the fingers of the N collectors with the finger vein infrared light source of the preset intensity, and collects the L persons of the N state under the current state (in the same regional environment). Refers to the vein image to obtain N*L finger vein images, where N≥2, L≥1.
S202：分别计算采集到的每一所述指静脉图像的截面像素灰度值的高斯分布参数；其中，每一所述指静脉图像的截面像素灰度值符合高斯分布，所述高斯分布参数包括像素灰度均值和标准差；S202: Calculate a Gaussian distribution parameter of the gray value of the cross-section pixel of each of the finger vein images collected respectively; wherein the gray value of the cross-section pixel of each of the finger vein images conforms to a Gaussian distribution, and the Gaussian distribution parameter includes Pixel gray mean and standard deviation;
该步骤用于对采集到的N*L个指静脉图像的灰度值进行统计。具体的，参见图4，该步骤可以通过以下步骤实现，包括步骤S2021-S2023，其中：This step is used to count the gray values of the collected N*L finger vein images. Specifically, referring to FIG. 4, the step may be implemented by the following steps, including steps S2021-S2023, where:
S2021：对每一所述指静脉图像进行二值化以划分出指静脉区域，基于所述指静脉区域得到指静脉线条；S2021: Perform binarization on each of the finger vein images to divide a finger vein region, and obtain a finger vein line based on the finger vein region;
具体的，采用NIBLACK图像二值化方法对每一所述指静脉图像进行二值化处理，从而提取指静脉区域。对提取的所述指静脉区域进行分割后，进行骨架提取，从而得到指静脉线条。Specifically, each of the finger vein images is binarized by using a NIBLACK image binarization method to extract a finger vein region. After the extracted finger vein region is divided, skeleton extraction is performed to obtain a finger vein line.
S2022：对每一所述指静脉线条取等间距的T个截面，取每个截面所有像素的灰度值，记为
Figure PCTCN2017087841-appb-000025
其中
Figure PCTCN2017087841-appb-000026
为第t个截面的第i个像素的灰度值，Mt为第t个截面所对应的像素个数，T≥3；其中，每一所述指静脉图像的截面像素灰度值符合高斯分布。S2022: taking equal intervals of T sections for each of the finger vein lines, taking the gray value of all the pixels of each section, and recording
Figure PCTCN2017087841-appb-000025
among them
Figure PCTCN2017087841-appb-000026
The gray value of the i-th pixel of the t-th cross-section, M t is the number of pixels corresponding to the t-th cross-section, T≥3; wherein the gray value of the cross-section pixel of each of the finger vein images conforms to Gauss distributed.
S2023：通过以下公式计算得到每一所述指静脉图像的截面像素灰度值的高斯分布参数(μ，σ)：S2023: Calculating a Gaussian distribution parameter (μ, σ) of the gray value of the cross-section pixel of each of the finger vein images by the following formula:
Figure PCTCN2017087841-appb-000027
Figure PCTCN2017087841-appb-000027
Figure PCTCN2017087841-appb-000028
Figure PCTCN2017087841-appb-000028
S203：根据每一所述指静脉图像的截面像素灰度值的高斯分布参数计算得到每一所述指静脉图像的宽度，然后计算所有指静脉图像的宽度的平均值hw，并选取所述N*L个指静脉图像中宽度在hw(1±A％)之间的所有指静脉图像；其中，0＜A≤50，每一所述指静脉图像的宽度是指每一所述指静脉图像上的截面像素灰度值小于其对应的像素灰度均值的像素个数；S203: Calculate a width of each of the finger vein images according to a Gaussian distribution parameter of a cross-sectional pixel gray value of each of the finger vein images, and then calculate an average value h w of widths of all the finger vein images, and select the All finger vein images having a width between h w (1±A%) in N*L finger vein images; wherein 0<A≤50, the width of each of the finger vein images refers to each of the fingers The number of pixels of the cross-section pixel gray value on the vein image is smaller than the corresponding pixel gray mean value;
该步骤用于对采集到的指静脉图像进行进一步的选取，选取宽度符合要求的指静脉图像。具体的， 在该步骤中，首先计算出每一个指静脉图像的宽度，然后求出宽度平均值hw，本实施例中，A＝10，即选取宽度为平均宽度的0.9倍至1.1倍的指静脉图像。This step is used to further select the collected finger vein image and select a finger vein image with a width that meets the requirements. Specifically, in this step, the width of each finger vein image is first calculated, and then the width average value h w is obtained. In this embodiment, A=10, that is, the selected width is 0.9 times to 1.1 times the average width. Refers to the vein image.
S204：计算所选取的所有指静脉图像的截面像素灰度值的高斯分布参数(μr，σr)；S204: calculating a Gaussian distribution parameter (μ r , σ r ) of the gray value of the cross-section pixel of all the finger vein images selected;
该步骤用于对选取的指静脉图像的灰度值进行统计。具体的，在该步骤中使用以下公式计算所选取的所有指静脉图像的截面像素灰度值的高斯分布参数(μr，σr)：This step is used to count the gray value of the selected finger vein image. Specifically, in this step, the Gaussian distribution parameter (μ r , σ r ) of the gray value of the cross-section pixel of all the finger vein images selected is calculated using the following formula:
μr＝(μ1+μ2+μ3+...+μp)/Pμ r =(μ 1 +μ 2 +μ 3 +...+μ p )/P
Figure PCTCN2017087841-appb-000029
Figure PCTCN2017087841-appb-000029
其中，P为所选取的所有指静脉图像的总个数，μ1，μ2，μ3，...，μp分别为所选取的每一指静脉图像的截面像素灰度值的像素灰度均值。Where P is the total number of all finger vein images selected, μ 1 , μ 2 , μ 3 , ..., μ p are respectively pixel gray values of the gray values of the cross-section pixels of each finger vein image selected Degree average.
S205：通过公式：
Figure PCTCN2017087841-appb-000030
计算所述红外图像传感器的伽马映射系数，其中，(μs，σs)、γs为所述指静脉识别装置预设的标准值，(μr，σr)为上述步骤S204计算得到的选取的指静脉图像截面像素灰度值的高斯分布参数，γ为所述红外图像传感器的伽马映射系数。S205: Pass the formula:
Figure PCTCN2017087841-appb-000030
Calculating a gamma mapping coefficient of the infrared image sensor, wherein (μ s , σ s ), γ s is a preset standard value of the finger vein recognition device, and (μ r , σ r ) is calculated by the above step S204 The selected Gaussian distribution parameter of the gray value of the cross-sectional image of the finger vein image, and γ is the gamma mapping coefficient of the infrared image sensor.
S206：根据计算得到的所述伽马映射系数γ，通过公式
Figure PCTCN2017087841-appb-000031
调整所述红外图像传感器获取的当前状态下的像素输入值f和像素输出值f1映射关系，然后通过所述指静脉识别装置对调整后的像素输出值f1进行识别以获取识别结果。S206: Pass the formula according to the calculated gamma mapping coefficient γ
Figure PCTCN2017087841-appb-000031
Adjusting the mapping relationship between the pixel input value f and the pixel output value f 1 in the current state acquired by the infrared image sensor, and then identifying the adjusted pixel output value f 1 by the finger vein recognition device to obtain the recognition result.
在该步骤中，根据步骤S205计算得到的伽马映射系数γ，即可通过预设的调整公式
Figure PCTCN2017087841-appb-000032
对指静脉识别装置通过红外图像传感器获取的当前状态下的图像像素进行校准，并将经过像素校准后的图像才进行指静脉识别，以得到识别结果。In this step, according to the gamma mapping coefficient γ calculated in step S205, the preset adjustment formula can be adopted.
Figure PCTCN2017087841-appb-000032
The image pixels in the current state acquired by the finger vein recognition device through the infrared image sensor are calibrated, and the pixel-calibrated image is subjected to finger vein recognition to obtain a recognition result.
如上所述实施例二，不同于实施例一的是在计算指静脉图像的截面像素灰度值的高斯分布参数时对指静脉图像进行了选取，在采集到的所有指静脉图像中优选了使实验结果更稳定的指静脉图像的宽度为平均宽度的0.9倍至1.1倍的指静脉图像，根据选取的指静脉图像的截面像素灰度值的高斯分布参数得到伽马映射系数，使用该伽马映射系数校准后的指静脉识别装置的识别成功率更高。As described in the second embodiment, unlike the first embodiment, the finger vein image is selected when calculating the Gaussian distribution parameter of the gray value of the cross-section pixel of the finger vein image, and it is preferable to select all the finger vein images collected. The experimental result is more stable. The width of the finger vein image is 0.9 to 1.1 times the average width of the finger vein image, and the gamma mapping coefficient is obtained according to the Gaussian distribution parameter of the gray value of the cross-section pixel of the selected finger vein image, and the gamma is used. The recognition success rate of the finger vein identification device after the mapping coefficient calibration is higher.
参见图7，是本发明实施例三提供的一种指静脉识别装置的校准方法的流程示意图，本发明实施例三提供的一种指静脉识别装置的校准方法包括步骤S301-S306：FIG. 7 is a schematic flowchart of a method for calibrating a finger vein recognition device according to Embodiment 3 of the present invention. The calibration method for a finger vein recognition device according to Embodiment 3 of the present invention includes steps S301-S306:
S301：利用指静脉识别装置的红外图像传感器采集当前状态下的N个人各L幅指静脉图像，其中N≥2，L≥1； S301: The infrared image sensor of the finger vein recognition device is used to collect the image of each finger vein of the N person in the current state, wherein N≥2, L≥1;
该步骤用于采集指静脉图像。具体的，在该步骤中，利用指静脉识别装置的红外图像传感器采用预设强度的指静脉红外光源照射N个采集者的手指，采集当前状态(在同一区域环境)下的N个人各L幅指静脉图像，从而得到N*L个指静脉图像，其中N≥2，L≥1。This step is used to acquire a finger vein image. Specifically, in this step, the infrared image sensor using the finger vein recognition device irradiates the fingers of the N collectors with the finger vein infrared light source of the preset intensity, and collects the L persons of the N state under the current state (in the same regional environment). Refers to the vein image to obtain N*L finger vein images, where N≥2, L≥1.
S302：分别计算采集到的每一所述指静脉图像的截面像素灰度值的高斯分布参数，其中，每一所述指静脉图像的截面像素灰度值符合高斯分布，所述高斯分布参数包括像素灰度均值和标准差；S302: Calculate a Gaussian distribution parameter of the gray value of the cross-section pixel of each of the finger vein images collected, wherein the gray value of the cross-section pixel of each of the finger vein images conforms to a Gaussian distribution, and the Gaussian distribution parameter includes Pixel gray mean and standard deviation;
该步骤用于对采集到的N*L个指静脉图像的灰度值进行统计。具体的，参见图6，该步骤可以通过以下步骤实现，包括步骤S3021-S3023，其中：This step is used to count the gray values of the collected N*L finger vein images. Specifically, referring to FIG. 6, the step may be implemented by the following steps, including steps S3021-S3023, where:
S3021：对每一所述指静脉图像进行二值化以划分出指静脉区域，基于所述指静脉区域得到指静脉线条；S3021: Perform binarization on each of the finger vein images to divide a finger vein region, and obtain a finger vein line based on the finger vein region;
具体的，采用NIBLACK图像二值化方法对每一所述指静脉图像进行二值化处理，从而提取指静脉区域。对提取的所述指静脉区域进行分割后，进行骨架提取，从而得到指静脉线条。Specifically, each of the finger vein images is binarized by using a NIBLACK image binarization method to extract a finger vein region. After the extracted finger vein region is divided, skeleton extraction is performed to obtain a finger vein line.
S3022：对每一所述指静脉线条取等间距的T个截面，取每个截面所有像素的灰度值，记为
Figure PCTCN2017087841-appb-000033
其中
Figure PCTCN2017087841-appb-000034
为第t个截面的第i个像素的灰度值，Mt为第t个截面所对应的像素个数，T≥3，其中，每一所述指静脉图像的截面像素灰度值符合高斯分布。S3022: Taking equal intervals of T sections for each of the finger vein lines, taking the gray value of all the pixels of each section, and recording
Figure PCTCN2017087841-appb-000033
among them
Figure PCTCN2017087841-appb-000034
The gray value of the i-th pixel of the t-th cross-section, M t is the number of pixels corresponding to the t-th cross-section, T≥3, wherein the gray value of the cross-section pixel of each of the finger vein images conforms to Gaussian distributed.
S3023：通过以下公式计算得到每一所述指静脉图像的截面像素灰度值的高斯分布参数(μ，σ)：S3023: Calculating a Gaussian distribution parameter (μ, σ) of the gray value of the cross-section pixel of each of the finger vein images by the following formula:
Figure PCTCN2017087841-appb-000035
Figure PCTCN2017087841-appb-000035
Figure PCTCN2017087841-appb-000036
Figure PCTCN2017087841-appb-000036
S303：根据每一所述指静脉图像的截面像素灰度值的高斯分布参数计算得到每一所述指静脉图像的宽度，然后计算所有指静脉图像的宽度的平均值hw，并选取所述N*L个指静脉图像中宽度在hw(1±A％)之间的所有指静脉图像；其中，0＜A≤50，每一所述指静脉图像的宽度是指每一所述指静脉图像上的截面像素灰度值小于其对应的像素灰度均值的像素个数。S303: Calculate a width of each of the finger vein images according to a Gaussian distribution parameter of a gray value of a cross-sectional pixel of each of the finger vein images, and then calculate an average value h w of widths of all the finger vein images, and select the All finger vein images having a width between h w (1±A%) in N*L finger vein images; wherein 0<A≤50, the width of each of the finger vein images refers to each of the fingers The grayscale value of the cross-section pixel on the vein image is smaller than the number of pixels of the corresponding grayscale mean of the pixel.
该步骤用于对采集到的指静脉图像进行进一步的选取，选取宽度符合要求的指静脉图像。具体的，在该步骤中，首先计算出每一个指静脉图像的宽度，然后求出宽度平均值hw，本实施例中，A＝10，即选取宽度为平均宽度的0.9倍至1.1倍的指静脉图像。This step is used to further select the collected finger vein image and select a finger vein image with a width that meets the requirements. Specifically, in this step, the width of each finger vein image is first calculated, and then the width average value h w is obtained. In this embodiment, A=10, that is, the selected width is 0.9 times to 1.1 times the average width. Refers to the vein image.
S304：计算所选取的所有指静脉图像的截面像素灰度值的高斯分布参数(μr，σr)；S304: calculating a Gaussian distribution parameter (μ r , σ r ) of the gray value of the cross-section pixel of all the finger vein images selected;
该步骤用于对选取的指静脉图像的灰度值进行统计。具体的，参见图7，该步骤可以通过以下步 骤实现，包括步骤S3041-S3042，其中：This step is used to count the gray value of the selected finger vein image. Specifically, referring to FIG. 7, the step can be as follows: The implementation includes steps S3041-S3042, wherein:
S3041：对每一所述指静脉线条取等间距的T2个截面，共得到B2＝P*T2个截面；取每个截面所有像素的灰度值，记为
Figure PCTCN2017087841-appb-000037
其中
Figure PCTCN2017087841-appb-000038
为第b2个截面的第i个像素的灰度值，Mb2为第b2个截面所对应的像素个数，P为所选取的所有指静脉图像的总个数，T2≥3。S3041: taking an equal spacing of T 2 sections for each of the finger vein lines, and obtaining a total of B 2 =P*T 2 sections; taking the gray value of all the pixels of each section,
Figure PCTCN2017087841-appb-000037
among them
Figure PCTCN2017087841-appb-000038
2 pixels of gray value of the i-th cross-section b, M b2 B is the number of pixels corresponding to the two-section, refers to the total number of all the vein image P is selected, T 2 ≥3.
S3042：通过以下公式计算所选取的所有指静脉图像的截面像素灰度值的高斯分布参数(μr，σr)S3042: Calculating a Gaussian distribution parameter (μ r , σ r ) of the gray value of the cross-section pixel of all the finger vein images selected by the following formula
Figure PCTCN2017087841-appb-000039
Figure PCTCN2017087841-appb-000039
Figure PCTCN2017087841-appb-000040
Figure PCTCN2017087841-appb-000040
S305：通过公式：
Figure PCTCN2017087841-appb-000041
计算所述红外图像传感器的伽马映射系数，其中，(μs，σs)、γs为所述指静脉识别装置预设的标准值，(μr，σr)为上述步骤S304计算得到的选取的指静脉图像的截面像素灰度值高斯分布参数，γ为所述红外图像传感器的伽马映射系数。S305: Pass the formula:
Figure PCTCN2017087841-appb-000041
Calculating a gamma mapping coefficient of the infrared image sensor, wherein (μ s , σ s ), γ s is a preset standard value of the finger vein recognition device, and (μ r , σ r ) is calculated by the above step S304 The cross-sectional pixel gray value Gaussian distribution parameter of the selected finger vein image, γ is the gamma mapping coefficient of the infrared image sensor.
S306：根据计算得到的所述伽马映射系数γ，通过公式
Figure PCTCN2017087841-appb-000042
调整所述红外图像传感器获取的当前状态下的像素输入值f和像素输出值f1映射关系，然后通过所述指静脉识别装置对调整后的像素输出值f1进行识别以获取识别结果。S306: According to the calculated gamma mapping coefficient γ, pass the formula
Figure PCTCN2017087841-appb-000042
Adjusting the mapping relationship between the pixel input value f and the pixel output value f 1 in the current state acquired by the infrared image sensor, and then identifying the adjusted pixel output value f 1 by the finger vein recognition device to obtain the recognition result.
在该步骤中，根据步骤S305计算得到的伽马映射系数γ，即可通过预设的调整公式
Figure PCTCN2017087841-appb-000043
对指静脉识别装置通过红外图像传感器获取的当前状态下的图像像素进行校准，并将经过像素校准后的图像才进行指静脉识别，以得到识别结果。In this step, according to the gamma mapping coefficient γ calculated in step S305, the preset adjustment formula can be adopted.
Figure PCTCN2017087841-appb-000043
The image pixels in the current state acquired by the finger vein recognition device through the infrared image sensor are calibrated, and the pixel-calibrated image is subjected to finger vein recognition to obtain a recognition result.
如上所述实施例三，不同于实施例一的是在计算指静脉图像的截面像素灰度值的高斯分布参数时对指静脉图像进行了选取，在采集到的所有指静脉图像中优选了使实验结果更稳定的指静脉图像的宽度为平均宽度的0.9倍至1.1倍的指静脉图像，根据选取的指静脉图像的截面像素灰度值的高斯分布参数得到伽马映射系数，使用该伽马映射系数校准后的指静脉识别装置的识别成功率更高。与实施例二的区别在于计算所选取的指静脉图像的截面像素灰度值的高斯分布参数时方法有所不同，实施例二是先求出每一幅选取的指静脉图像的界面像素灰度值高斯分布参数，再计算所有指静脉图像的高斯分布参数，而实施例三则是直接求出所选取的所有指静脉截面像素灰度值的高斯分布参数。As described in the third embodiment, unlike the first embodiment, the finger vein image is selected when calculating the Gaussian distribution parameter of the gray value of the cross-section pixel of the finger vein image, and it is preferable to select all the finger vein images collected. The experimental result is more stable. The width of the finger vein image is 0.9 to 1.1 times the average width of the finger vein image, and the gamma mapping coefficient is obtained according to the Gaussian distribution parameter of the gray value of the cross-section pixel of the selected finger vein image, and the gamma is used. The recognition success rate of the finger vein identification device after the mapping coefficient calibration is higher. The difference from the second embodiment is that the method for calculating the Gaussian distribution parameter of the cross-sectional pixel gray value of the selected finger vein image is different. The second embodiment is to first find the interface pixel gray scale of each selected finger vein image. The Gaussian distribution parameter is calculated, and the Gaussian distribution parameters of all the finger vein images are calculated. In the third embodiment, the Gaussian distribution parameters of all the gray values of the finger vein segments are directly obtained.
以上所述是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离 本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也视为本发明的保护范围。 The above is a preferred embodiment of the present invention, it should be noted that one of ordinary skill in the art will not be able to Several modifications and refinements can also be made on the premise of the principles of the invention, which are also considered to be within the scope of the invention.

Claims (10)

  1. 一种指静脉识别装置的校准方法，其特征在于，包括步骤：A calibration method for a finger vein recognition device, comprising the steps of:
    S1、利用指静脉识别装置的红外图像传感器采集当前状态下的N个人各L幅指静脉图像，其中N≥2，L≥1；S1, using an infrared image sensor of the finger vein recognition device to collect L finger images of N individuals in the current state, wherein N≥2, L≥1;
    S2、计算采集到的所述N*L个指静脉图像的截面像素灰度值的高斯分布参数(μ，σ)；其中，μ为均值，σ为标准差，每一所述指静脉图像的截面像素灰度值符合高斯分布；S2. Calculating a Gaussian distribution parameter (μ, σ) of the gray value of the cross-section pixel of the collected N*L finger vein images; wherein μ is a mean value, and σ is a standard deviation, and each of the finger vein images is The gray value of the cross-section pixel conforms to the Gaussian distribution;
    S3、通过以下公式计算所述红外图像传感器的伽马映射系数：S3. Calculate a gamma mapping coefficient of the infrared image sensor by using the following formula:
    Figure PCTCN2017087841-appb-100001
    Figure PCTCN2017087841-appb-100001
    其中，(μs，σs)、γs为所述指静脉识别装置预设的标准值，(μ，σ)为上述步骤得到的高斯分布参数，γ为所述红外图像传感器的伽马映射系数；Wherein (μ s , σ s ), γ s is a preset standard value of the finger vein recognition device, (μ, σ) is a Gaussian distribution parameter obtained by the above step, and γ is a gamma map of the infrared image sensor. coefficient;
    S4、根据计算得到的所述伽马映射系数γ，调整所述红外图像传感器获取的当前状态下的像素输入值f和像素输出值f1映射关系，然后通过所述指静脉识别装置对调整后的像素输出值f1进行识别以获取识别结果。S4, the map of the gamma coefficient γ calculated, adjusting f. 1 mapping between the input pixel f and pixel values of the current state of the output values of the infrared image sensor is acquired, then the vein authentication device adjusted by said means The pixel output value f 1 is identified to obtain the recognition result.
  2. 如权利要求1所述的指静脉识别装置的校准方法，其特征在于，所述步骤S2具体包括步骤：The method for calibrating a finger vein recognition device according to claim 1, wherein the step S2 comprises the following steps:
    S21、对每一所述指静脉图像进行二值化以划分出指静脉区域，基于所述指静脉区域得到指静脉线条；S21: Perform binarization on each of the finger vein images to divide a finger vein region, and obtain a finger vein line based on the finger vein region;
    S22、对每一所述指静脉线条取等间距的T个截面，共得到B＝N*L*T个截面；取每个截面所有像素的灰度值，记为
    Figure PCTCN2017087841-appb-100002
    其中
    Figure PCTCN2017087841-appb-100003
    为第b个截面的第i个像素的灰度值，Mb为第b个截面所对应的像素个数，T≥3；    S22, taking T sections of equal intervals for each of the finger vein lines, and obtaining a total of B=N*L*T sections; taking the gray value of all the pixels of each section,
    Figure PCTCN2017087841-appb-100002
    among them
    Figure PCTCN2017087841-appb-100003
    Is the gray value of the i-th pixel of the b-th cross-section, and M b is the number of pixels corresponding to the b-th cross-section, T≥3;
    S23、通过以下公式计算得到所述B个截面像素灰度值的高斯分布参数(μ，σ)：S23. Calculate a Gaussian distribution parameter (μ, σ) of the gray values of the B cross-section pixels by using the following formula:
    Figure PCTCN2017087841-appb-100004
    Figure PCTCN2017087841-appb-100004
    Figure PCTCN2017087841-appb-100005
    Figure PCTCN2017087841-appb-100005
  3. 如权利要求1或2所述的指静脉识别装置的校准方法，其特征在于，在步骤S4中，通过公式
    Figure PCTCN2017087841-appb-100006
    调整所述红外图像传感器获取的当前状态下的像素输入值f和像素输出值f1映射关系。    A method of calibrating a finger vein recognition device according to claim 1 or 2, wherein in step S4, a formula is adopted
    Figure PCTCN2017087841-appb-100006
    Adjusting a mapping relationship between the pixel input value f and the pixel output value f 1 in the current state acquired by the infrared image sensor.
  4. 一种指静脉识别装置的校准方法，其特征在于，包括步骤：A calibration method for a finger vein recognition device, comprising the steps of:
    S1、利用指静脉识别装置的红外图像传感器采集当前状态下的N个人各L幅指静脉图像，其中N≥2，L≥1；S1, using an infrared image sensor of the finger vein recognition device to collect L finger images of N individuals in the current state, wherein N≥2, L≥1;
    S2、分别计算采集到的每一所述指静脉图像的截面像素灰度值的高斯分布参数；其中，每一所述指静脉图像的截面像素灰度值符合高斯分布，所述高斯分布参数包括像素灰度均值和标准差；S2, respectively calculating a Gaussian distribution parameter of the gray value of the cross-section pixel of each of the finger vein images collected; wherein, the gray value of the cross-section pixel of each of the finger vein images conforms to a Gaussian distribution, and the Gaussian distribution parameter includes Pixel gray mean and standard deviation;
    S3、根据每一所述指静脉图像的截面像素灰度值的高斯分布参数计算得到每一所述指静脉图像的宽度，然后计算所有指静脉图像的宽度的平均值hw，并选取所述N*L个指静脉图像中宽度在hw(1±A％)之间的所有指静脉图像；其中，0＜A≤50，每一所述指静脉图像的宽度是指每一所述指静脉图像上的截面像素灰度值小于其对应的像素灰度均值的像素个数；S3. Calculate a width of each of the finger vein images according to a Gaussian distribution parameter of a gray value of a cross-section pixel of each of the finger vein images, and then calculate an average value h w of widths of all the finger vein images, and select the All finger vein images having a width between h w (1±A%) in N*L finger vein images; wherein 0<A≤50, the width of each of the finger vein images refers to each of the fingers The number of pixels of the cross-section pixel gray value on the vein image is smaller than the corresponding pixel gray mean value;
    S4、计算所选取的所有指静脉图像的截面像素灰度值的高斯分布参数(μr，σr)；S4. Calculate a Gaussian distribution parameter (μ r , σ r ) of the gray value of the cross-section pixel of all the finger vein images selected;
    S5、通过以下公式计算所述红外图像传感器的伽马映射系数：S5. Calculate a gamma mapping coefficient of the infrared image sensor by using the following formula:
    Figure PCTCN2017087841-appb-100007
    Figure PCTCN2017087841-appb-100007
    其中，(μs，σs)、γs为所述指静脉识别装置预设的标准值，(μr，σr)为上述步骤得到的高斯分布参数，γ为所述红外图像传感器的伽马映射系数；Wherein (μ s , σ s ), γ s is a preset standard value of the finger vein recognition device, (μ r , σ r ) is a Gaussian distribution parameter obtained by the above step, and γ is a gamma of the infrared image sensor Horse mapping coefficient;
    S6、根据计算得到的所述伽马映射系数γ，调整所述红外图像传感器获取的当前状态下的像素输入值f和像素输出值f1映射关系，然后通过所述指静脉识别装置对调整后的像素输出值f1进行识别以获取识别结果。 S6, according to the mapping coefficient γ calculated the gamma adjusted values of the input pixel f and pixel output value f. 1 mapping relationship in the current state of the infrared image sensor is acquired, then the vein authentication device adjusted by said means The pixel output value f 1 is identified to obtain the recognition result.
  5. 如权利要求4所述的指静脉识别装置的校准方法，其特征在于，所述步骤S2具体包括步骤：The method for calibrating a finger vein identification device according to claim 4, wherein the step S2 comprises the following steps:
    S21、对每一所述指静脉图像进行二值化以划分出指静脉区域，基于所述指静脉区域得到指静脉线条；S21: Perform binarization on each of the finger vein images to divide a finger vein region, and obtain a finger vein line based on the finger vein region;
    S22、对每一所述指静脉线条取等间距的T个截面，取每个截面所有像素的灰度值，记为
    Figure PCTCN2017087841-appb-100008
    其中
    Figure PCTCN2017087841-appb-100009
    为第t个截面的第i个像素的灰度值，Mt为第t个截面所对应的像素个数，T≥3；    S22. Taking equal intervals of T sections for each of the finger vein lines, taking the gray value of all the pixels of each section, and recording
    Figure PCTCN2017087841-appb-100008
    among them
    Figure PCTCN2017087841-appb-100009
    The gray value of the i-th pixel of the t-th cross-section, M t is the number of pixels corresponding to the t-th cross-section, T≥3;
    S23、通过以下公式计算得到每一所述指静脉图像的截面像素灰度值的高斯分布参数(μ，σ)：S23. Calculate a Gaussian distribution parameter (μ, σ) of the gray value of the cross-section pixel of each of the finger vein images by using the following formula:
    Figure PCTCN2017087841-appb-100010
    Figure PCTCN2017087841-appb-100010
    Figure PCTCN2017087841-appb-100011
    Figure PCTCN2017087841-appb-100011
  6. 如权利要求5所述的指静脉识别装置的校准方法，其特征在于，在步骤S4中，通过以下公式计算所选取的所有指静脉图像的截面像素灰度值的高斯分布参数(μr，σr)：The calibration method of finger vein recognition apparatus as claimed in claim 5, wherein, in the step S4, the selected Gaussian distribution parameter calculated by the following equation refers to all the pixel gray value cross-section of the vein image (μ r, σ r ):
    μr＝(μ1+μ2+μ3+...+μp)/Pμ r =(μ 1 +μ 2 +μ 3 +...+μ p )/P
    Figure PCTCN2017087841-appb-100012
    Figure PCTCN2017087841-appb-100012
    其中，P为所选取的所有指静脉图像的总个数，μ1，μ2，μ3，...，μp分别为所选取的每一指静脉图像的截面像素灰度值的像素灰度均值。Where P is the total number of all finger vein images selected, μ 1 , μ 2 , μ 3 , ..., μ p are respectively pixel gray values of the gray values of the cross-section pixels of each finger vein image selected Degree average.
  7. 如权利要求5所述的指静脉识别装置的校准方法，其特征在于，在步骤S4中，通过以下步骤计算所选取的所有指静脉图像的截面像素灰度值的高斯分布参数(μr，σr)：The calibration method of finger vein recognition apparatus as claimed in claim 5, wherein, in the step S4, the Gaussian distribution parameter calculated by the steps of selecting all pixel gray value refers to a cross-sectional image of the vein (μ r, σ r ):
    S41、对每一所述指静脉线条取等间距的T2个截面，共得到B2＝P*T2个截面；取每个截面所有像素的灰度值，记为
    Figure PCTCN2017087841-appb-100013
    其中
    Figure PCTCN2017087841-appb-100014
    为第b2个截面的第i个像素的灰度值，Mb2为第b2 个截面所对应的像素个数，P为所选取的所有指静脉图像的总个数，T2≥3；    S41, taking an equal spacing of T 2 sections for each of the finger vein lines, and obtaining a total of B 2 =P*T 2 sections; taking the gray value of all the pixels of each section,
    Figure PCTCN2017087841-appb-100013
    among them
    Figure PCTCN2017087841-appb-100014
    2 is a cross section of a pixel gradation value b i-th, M b2 b is the number of pixels corresponding to the two-section, refers to the total number of all the vein image P is selected, T 2 ≥3;
    S42、通过以下公式计算所选取的所有指静脉图像的截面像素灰度值的高斯分布参数(μr，σr)：S42. Calculate a Gaussian distribution parameter (μ r , σ r ) of the gray value of the cross-section pixel of all the finger vein images selected by the following formula:
    Figure PCTCN2017087841-appb-100015
    Figure PCTCN2017087841-appb-100015
    Figure PCTCN2017087841-appb-100016
    Figure PCTCN2017087841-appb-100016
  8. 如权利要求5所述的指静脉识别装置的校准方法，其特征在于，所述步骤S21具体包括：The method of calibrating a finger vein identification device according to claim 5, wherein the step S21 specifically comprises:
    S211、采用NIBLACK图像二值化方法对每一所述指静脉图像进行二值化处理，从而提取指静脉区域；S211, performing a binarization process on each of the finger vein images by using a NIBLACK image binarization method, thereby extracting a finger vein region;
    S212、对提取的所述指静脉区域进行分割后，进行骨架提取，得到指静脉线条。S212. After dividing the extracted finger vein region, the skeleton is extracted to obtain a finger vein line.
  9. 如权利要求4或5所述的指静脉识别装置的校准方法，其特征在于，在步骤S3中，A＝10。A method of calibrating a finger vein recognition device according to claim 4 or 5, wherein in step S3, A = 10.
  10. 如权利要求4或5所述的指静脉识别装置的校准方法，其特征在于，在步骤S6中，通过公式
    Figure PCTCN2017087841-appb-100017
    调整所述红外图像传感器获取的当前状态下的像素输入值f和像素输出值f1映射关系。     A method of calibrating a finger vein recognition device according to claim 4 or 5, wherein in step S6, a formula is adopted
    Figure PCTCN2017087841-appb-100017
    Adjusting a mapping relationship between the pixel input value f and the pixel output value f 1 in the current state acquired by the infrared image sensor.
PCT/CN2017/087841 2016-09-13 2017-06-10 Calibration method for finger vein identification apparatus WO2018049858A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201610820854.3 2016-09-13
CN201610820854.3A CN106446813B (en) 2016-09-13 2016-09-13 A kind of calibration method referring to vein identification device

Publications (1)

Publication Number Publication Date
WO2018049858A1 true WO2018049858A1 (en) 2018-03-22

Family

ID=58168857

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2017/087841 WO2018049858A1 (en) 2016-09-13 2017-06-10 Calibration method for finger vein identification apparatus

Country Status (2)

Country Link
CN (1) CN106446813B (en)
WO (1) WO2018049858A1 (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110826513A (en) * 2019-11-13 2020-02-21 圣点世纪科技股份有限公司 Calibration algorithm for consistency of finger vein equipment

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106446813B (en) * 2016-09-13 2019-10-11 广州广电运通金融电子股份有限公司 A kind of calibration method referring to vein identification device
CN110188675B (en) * 2019-05-29 2021-04-13 Oppo广东移动通信有限公司 Vein collection method and related products
CN117631717B (en) * 2024-01-10 2024-06-28 深圳豪达尔机械有限公司 Sensor control and regulation system of variable-frequency cold dryer

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6763127B1 (en) * 2000-10-06 2004-07-13 Ic Media Corporation Apparatus and method for fingerprint recognition system
CN101331515A (en) * 2005-12-21 2008-12-24 日本电气株式会社 Gray-scale correcting method, gray-scale correcting device, gray-scale correcting program, and image device
CN101650439A (en) * 2009-08-28 2010-02-17 西安电子科技大学 Method for detecting change of remote sensing image based on difference edge and joint probability consistency
JP2011091595A (en) * 2009-10-22 2011-05-06 Kyocera Mita Corp Image processor, image processing method and image forming apparatus
CN104794689A (en) * 2015-03-12 2015-07-22 哈尔滨工程大学 Preprocessing method for enhancing sonar image contract
CN106446813A (en) * 2016-09-13 2017-02-22 广州广电运通金融电子股份有限公司 Calibration method for finger vein identification device

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4341597B2 (en) * 2005-08-25 2009-10-07 セイコーエプソン株式会社 Gamma curve adjustment device and adjustment point setting method
CN101982826B (en) * 2010-11-10 2013-03-06 中国船舶重工集团公司第七一○研究所 Finger vein collection and identification method capable of automatically adjusting brightness of light source
CN103996209B (en) * 2014-05-21 2017-01-11 北京航空航天大学 Infrared vessel object segmentation method based on salient region detection
CN104688184B (en) * 2014-12-05 2017-08-04 南京航空航天大学 The vein developing method of visible ray skin image

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6763127B1 (en) * 2000-10-06 2004-07-13 Ic Media Corporation Apparatus and method for fingerprint recognition system
CN101331515A (en) * 2005-12-21 2008-12-24 日本电气株式会社 Gray-scale correcting method, gray-scale correcting device, gray-scale correcting program, and image device
CN101650439A (en) * 2009-08-28 2010-02-17 西安电子科技大学 Method for detecting change of remote sensing image based on difference edge and joint probability consistency
JP2011091595A (en) * 2009-10-22 2011-05-06 Kyocera Mita Corp Image processor, image processing method and image forming apparatus
CN104794689A (en) * 2015-03-12 2015-07-22 哈尔滨工程大学 Preprocessing method for enhancing sonar image contract
CN106446813A (en) * 2016-09-13 2017-02-22 广州广电运通金融电子股份有限公司 Calibration method for finger vein identification device

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110826513A (en) * 2019-11-13 2020-02-21 圣点世纪科技股份有限公司 Calibration algorithm for consistency of finger vein equipment
CN110826513B (en) * 2019-11-13 2022-04-19 圣点世纪科技股份有限公司 Calibration method for consistency of finger vein equipment

Also Published As

Publication number Publication date
CN106446813A (en) 2017-02-22
CN106446813B (en) 2019-10-11

Similar Documents

Publication Publication Date Title
Miao et al. A semi-automatic method for road centerline extraction from VHR images
WO2018049858A1 (en) Calibration method for finger vein identification apparatus
US11450152B2 (en) Detection of manipulated images
WO2019109526A1 (en) Method and device for age recognition of face image, storage medium
CN113159147B (en) Image recognition method and device based on neural network and electronic equipment
Varshney Improved NDBI differencing algorithm for built-up regions change detection from remote-sensing data: an automated approach
WO2017031716A1 (en) Method for analyzing and recognizing handwritten mathematical formula structure in natural scene image
WO2017166137A1 (en) Method for multi-task deep learning-based aesthetic quality assessment on natural image
KR101244220B1 (en) Apparatus and method for recognizing fingerprint
CN112507922A (en) Face living body detection method and device, electronic equipment and storage medium
CN111222380B (en) Living body detection method and device and recognition model training method thereof
EP2104059A1 (en) Apparatus and method for processing image
CN104408780A (en) Face recognition attendance system
WO2021189853A1 (en) Flash light spot position recognition method and apparatus, and electronic device and storage medium
CN112613471B (en) Face living body detection method, device and computer readable storage medium
CN106372624A (en) Human face recognition method and human face recognition system
WO2020143165A1 (en) Reproduced image recognition method and system, and terminal device
CN111260645A (en) Method and system for detecting tampered image based on block classification deep learning
Tirado et al. Bruise dating using deep learning
Justiawan et al. Comparative analysis of color matching system for teeth recognition using color moment
Moon et al. Age‐dependent skin texture analysis and evaluation using mobile camera image
WO2018107574A1 (en) Method and device for detecting see-through register anti-counterfeiting characteristics
Cao et al. Automatic latent value determination
CN103617423B (en) Image segmentation and recognition method based on color parameter
WO2016080815A1 (en) Method for inspecting forged passport and recording medium therefor

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17850063

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 29/07/2019)

122 Ep: pct application non-entry in european phase

Ref document number: 17850063

Country of ref document: EP

Kind code of ref document: A1