WO2018137226A1

WO2018137226A1 - Fingerprint extraction method and device

Info

Publication number: WO2018137226A1
Application number: PCT/CN2017/072712
Authority: WO
Inventors: 杨德培
Original assignee: 深圳市汇顶科技股份有限公司
Priority date: 2017-01-25
Filing date: 2017-01-25
Publication date: 2018-08-02
Also published as: CN107077617B; CN107077617A

Abstract

The present invention relates to the field of fingerprint recognition. Provided are a fingerprint extraction method and device. The fingerprint extraction method comprises : determining whether pressing of a finger is detected (101); if so, acquiring a sensing value of each pixel in a fingerprint sensing region (102); performing, according to the sensing value of each pixel and a Gaussian mixture background model preset for each pixel, recognition to obtain a pattern feature corresponding to each pixel (103); and generating, according to the pattern feature corresponding to each pixel, a fingerprint image of the finger (104). The method extracts a fingerprint image on the basis of a Gaussian mixture background model, thereby enabling extraction of a high-quality fingerprint image.

Description

指纹提取方法及装置Fingerprint extraction method and device

技术领域Technical field

本发明实施例涉及指纹识别技术领域，特别涉及一种指纹提取方法及装置。Embodiments of the present invention relate to the field of fingerprint identification technologies, and in particular, to a fingerprint extraction method and apparatus.

背景技术Background technique

如今，随着智能终端设备的飞速发展，指纹识别技术广泛应用于手机、平板电脑等电子设备，指纹识别成为了手机解锁、移动支付等的主要方式之一，给人们的生活带来极大的便利；而指纹提取作为指纹识别技术中很重要的环节，指纹提取的好坏直接影响到指纹识别的准确性。Nowadays, with the rapid development of smart terminal devices, fingerprint recognition technology is widely used in electronic devices such as mobile phones and tablet computers. Fingerprint recognition has become one of the main ways of unlocking mobile phones and mobile payment, which brings great life to people. Convenience; fingerprint extraction is an important part of fingerprint recognition technology. The quality of fingerprint extraction directly affects the accuracy of fingerprint recognition.

在实现本发明过程中，发明人发现现有技术中至少存在如下问题：In the process of implementing the present invention, the inventors have found that at least the following problems exist in the prior art:

在现有技术中，指纹提取通常是通过指纹感应芯片读取两次感应数据，一次是手指未触摸状态下芯片对应的像素的值，另一次是手指触摸状态下的芯片对应像素的值，通过对比两次数据的差异得到指纹图像。传统的做法是通过两次感应图像相减得到指纹图像。然而，由于温度等外界因素的影响，通过指纹感应芯片读取的感应数据并不稳定，因此传统方法通过两次感应数据作差所得到的指纹图像含有大量的噪声，甚至得不到指纹图像。In the prior art, fingerprint extraction usually reads the sensing data twice by the fingerprint sensing chip, one value of the pixel corresponding to the chip in the state where the finger is not touched, and the value of the corresponding pixel of the chip in the state of the finger touch. The fingerprint image is obtained by comparing the difference between the two data. The traditional approach is to obtain a fingerprint image by subtracting two sensing images. However, due to external factors such as temperature, the sensing data read by the fingerprint sensing chip is not stable. Therefore, the fingerprint image obtained by the conventional method by using the two sensing data to make a difference contains a large amount of noise, and even a fingerprint image is not obtained.

发明内容Summary of the invention

本发明实施方式的目的在于提供一种指纹提取方法及装置，基于混合高斯背景模型提取指纹图像，能够提取高质量的指纹图像。An object of the present invention is to provide a fingerprint extraction method and apparatus, which can extract a fingerprint image based on a mixed Gaussian background model and can extract a high quality fingerprint image.

为解决上述技术问题，本发明的实施方式提供了一种指纹提取方法，包括：当检测到手指触摸时，获取指纹感应区域中每个像素点的感应像素值；根据每个像素点的感应像素值与预设的每个像素点的混合高斯背景模型，识别每个像素点对应的纹路特征；根据每个像素点对应的纹路特征，生成手指的指纹图像。In order to solve the above technical problem, an embodiment of the present invention provides a fingerprint extraction method, and a package Include: when detecting a finger touch, acquiring a sensing pixel value of each pixel in the fingerprint sensing area; identifying each pixel according to a sensing Gaussian background model of each pixel point and a preset Gaussian background model of each pixel point A corresponding texture feature of the point; a fingerprint image of the finger is generated according to the texture feature corresponding to each pixel point.

本发明的实施方式还提供了一种指纹提取装置，包括：像素值获取模块，用于在检测到手指触摸时，获取指纹感应区域中每个像素点的感应像素值；纹路特征识别模块，用于根据每个像素点的感应像素值与预设的每个像素点的混合高斯背景模型，识别每个像素点对应的纹路特征；指纹图像生成模块，用于根据每个像素点对应的纹路特征，生成手指的指纹图像。The embodiment of the present invention further provides a fingerprint extraction device, comprising: a pixel value acquisition module, configured to acquire a sensing pixel value of each pixel in the fingerprint sensing area when detecting a finger touch; and a texture feature recognition module, And identifying a texture feature corresponding to each pixel point according to the mixed Gaussian background model of each pixel point and a preset pixel image generation module; and the fingerprint image generation module is configured to: according to the texture feature corresponding to each pixel point , generating a fingerprint image of the finger.

本发明实施方式相对于现有技术而言，由获取的手指触摸时指纹感应区域中每个像素点的感应像素值和预设的每个像素点的混合高斯背景模型，然后，识别每个像素点对应的纹路特征，将这些纹路特征按照像素点排列，生成手指的指纹图像；混合高斯背景模型对不稳定的像素具有很好的描述效果，利用混合高斯背景模型提取高质量的指纹图像。Compared with the prior art, the sensing pixel value of each pixel in the fingerprint sensing area and the preset mixed Gaussian background model of each pixel point are touched by the acquired finger, and then each pixel is identified. According to the texture features of the points, the texture features are arranged according to the pixel points to generate the fingerprint image of the finger; the mixed Gaussian background model has a good description effect on the unstable pixels, and the high-quality fingerprint image is extracted by the mixed Gaussian background model.

另外，在根据每个像素点的感应像素值与预设的每个像素点的混合高斯背景模型，识别每个像素点对应的纹路特征中，具体包括：对于每个像素点，判断像素点的感应像素值与像素点的混合高斯背景模型是否匹配；当像素点的感应像素值与像素点的混合高斯背景模型匹配时，将像素点的纹路特征识别为凹纹路。本实施例提供了一种具体的识别方式；即，凹纹路的像素点的感应像素值是与预设的像素点的混合高斯背景模型相匹配的，据此获取指纹图像的凹纹路。In addition, in the texture Gaussian background model of each pixel point and the preset Gaussian background model of each pixel point, identifying the texture feature corresponding to each pixel point specifically includes: determining, for each pixel point, the pixel point Whether the sensed pixel value matches the mixed Gaussian background model of the pixel; when the sensed pixel value of the pixel matches the mixed Gaussian background model of the pixel, the texture feature of the pixel is recognized as a concave path. This embodiment provides a specific identification manner; that is, the sensing pixel value of the pixel of the concave road is matched with the mixed Gaussian background model of the preset pixel, and the concave path of the fingerprint image is obtained accordingly.

另外，在根据每个像素点的感应像素值与预设的每个像素点的混合高斯背景模型，识别每个像素点对应的纹路特征中，还包括：当像素点的感应像素值与像素点的混合高斯背景模型不匹配时，判断像素点的感应像素值是否满足预设的凸纹路匹配条件；当像素点的感应像素值满足凸纹路匹配条件时，将像素点的纹路特征识别为凸纹路；当像素点的感应像素值不满足凸纹路匹配条件时，将像素点的纹路特征识别为凹纹路。即，当像素点的感应像素值与预设的像素点的混合高斯背景模型不匹配，但满足预设的凸纹路匹配条件，则认为像素点的纹路特征为凸纹路，否则，像素点的纹路特征为凹纹路；本实施例进一步完善了上述具体的识别方式；据此能够同时获取指纹图像的凸纹路与凹纹路，以获取完整的指纹图像。In addition, according to the mixed Gaussian background model of each pixel point and the preset Gaussian background model of each pixel point, identifying the texture feature corresponding to each pixel point further includes: when the pixel value of the pixel point and the pixel point When the mixed Gaussian background model does not match, it is determined whether the sensed pixel value of the pixel meets the preset ridge matching condition; when the sensed pixel value of the pixel satisfies the ridge matching condition The texture feature of the pixel is recognized as a ridge path; when the sensed pixel value of the pixel does not satisfy the ridge matching condition, the texture feature of the pixel is recognized as a concave path. That is, when the sensed pixel value of the pixel does not match the mixed Gaussian background model of the preset pixel, but the preset ridge matching condition is satisfied, the texture feature of the pixel is considered to be a ridge, otherwise, the texture of the pixel The feature is a concave road; the embodiment further improves the above specific recognition mode; accordingly, the convex road and the concave road of the fingerprint image can be simultaneously acquired to obtain a complete fingerprint image.

另外，混合高斯背景模型包括依次排列的若干个高斯成分；判断像素点的感应像素值与像素点的混合高斯背景模型是否匹配中，具体为：将像素点的感应像素值与若干个高斯成分依次比对，判断是否存在一个与像素点的感应像素值匹配的高斯成分；其中，当存在一个与像素点的感应像素值匹配的高斯成分时，判定像素点的感应像素值与像素点的混合高斯背景模型匹配；当不存在与像素点的感应像素值相匹配的高斯成分时，判定像素点的感应像素值与像素点的混合高斯背景模型不匹配。本实施例提供了判断像素点的感应像素值与像素点的混合高斯背景模型是否匹配的具体方式；其中，混合高斯背景模型包括若干个高斯成分，能够有效描述感应像素值多峰状态。In addition, the mixed Gaussian background model includes several Gaussian components arranged in sequence; determining whether the sensing pixel value of the pixel point matches the mixed Gaussian background model of the pixel point, specifically: sequentially sensing pixel values of the pixel points and several Gaussian components Aligning, determining whether there is a Gaussian component matching the sensed pixel value of the pixel; wherein, when there is a Gaussian component matching the sensed pixel value of the pixel, determining the mixed pixel value of the pixel point and the mixed Gauss of the pixel The background model matches; when there is no Gaussian component matching the sensed pixel value of the pixel, it is determined that the sensed pixel value of the pixel does not match the mixed Gaussian background model of the pixel. The embodiment provides a specific manner for determining whether the sensing pixel value of the pixel point matches the mixed Gaussian background model of the pixel point; wherein the mixed Gaussian background model includes several Gaussian components, which can effectively describe the multi-peak state of the sensing pixel value.

另外，将像素点的感应像素值与若干个高斯成分依次比对，判断是否存在一个与像素点的感应像素值匹配的高斯成分中，具体包括：计算像素点的感应像素值与每个高斯成分中的样本均值的差值，并获取绝对值最小的差值作为匹配参数；判断匹配参数是否小于或等于预设的第一阈值；其中，当匹配参数小于或等于预设的第一阈值时，判定存在一个与像素点的感应像素值匹配的高斯成分；当匹配参数大于第一阈值时，判定不存在与像素点的感应像素值匹配的高斯成分；凸纹路匹配条件包括：像素点的感应像素值大于预设的第二阈值；第二阈值大于第一阈值。本实施例是对上述判断是否匹配的具体方式的进一步细化；其中，将高斯成分中的样本均值与像素点的感应像素值之差的绝对值中最小的差值作为匹配参数，进一步提高了判定的准确性。 In addition, the sensing pixel value of the pixel is sequentially compared with the plurality of Gaussian components, and determining whether there is a Gaussian component matching the sensing pixel value of the pixel point includes: calculating the sensing pixel value of the pixel point and each Gaussian component. The difference between the sample mean values and the difference with the smallest absolute value as the matching parameter; determining whether the matching parameter is less than or equal to the preset first threshold; wherein, when the matching parameter is less than or equal to the preset first threshold, Determining that there is a Gaussian component matching the sensed pixel value of the pixel; when the matching parameter is greater than the first threshold, determining that there is no Gaussian component matching the sensed pixel value of the pixel; the ridge matching condition includes: sensing pixel of the pixel The value is greater than a preset second threshold; the second threshold is greater than the first threshold. This embodiment further refines the specific manner of whether the above determination is matched. The minimum difference between the absolute value of the difference between the sample mean value of the Gaussian component and the sensed pixel value of the pixel point is used as the matching parameter, which further improves the matching method. The accuracy of the decision.

另外，在根据每个像素点对应的纹路特征，生成手指的指纹图像之后，还包括：当检测到无手指触摸时，更新每个像素点的混合高斯背景模型。本实施例中，不断对预设每个像素点的混合高斯背景模型进行更新，以保证环境变化时，及时更新混合高斯背景模型。In addition, after generating the fingerprint image of the finger according to the texture feature corresponding to each pixel, the method further includes: updating the mixed Gaussian background model of each pixel when the fingerless touch is detected. In this embodiment, the mixed Gaussian background model of each pixel is preset to update the mixed Gaussian background model when the environment changes.

另外，每个像素点的混合高斯背景模型的预设方式，具体包括：创建像素点的混合高斯模型；混合高斯模型包括依次排列的多个高斯成分；根据多次获取的像素点的基础像素值，对混合高斯模型进行多次学习更新；其中，像素点的基础像素值为在无手指触摸时获取；对多次学习更新后的混合高斯模型中的多个高斯成分的权值进行归一化处理；根据预设选取规则，从归一化处理后的多个高斯成分中选取若干个高斯成分，以形成混合高斯背景模型。本实施例中提供了预设混合高斯背景模型的一种具体实现方式，满足实际设计需求。In addition, the preset manner of the mixed Gaussian background model of each pixel specifically includes: a mixed Gaussian model for creating pixel points; the mixed Gaussian model includes a plurality of Gaussian components arranged in sequence; and a base pixel value according to the plurality of acquired pixel points Multi-learning update of the mixed Gaussian model; wherein the basic pixel value of the pixel is acquired without finger touch; normalizing the weights of multiple Gaussian components in the mixed Gaussian model after multiple learning updates Processing; according to the preset selection rule, selecting a plurality of Gaussian components from the plurality of Gaussian components after the normalization to form a mixed Gaussian background model. In this embodiment, a specific implementation manner of the preset mixed Gaussian background model is provided to meet actual design requirements.

另外，学习更新的方式，具体包括：将像素点的基础像素值与依次排列的多个高斯成分依次比对，判断是否存在一个与像素点的基础像素值匹配的高斯成分；当存在一个与像素点的基础像素值匹配的高斯成分时，根据预设的权值增量更新高斯成分的权值，并根据像素点的基础像素值更新高斯成分的样本均值与样本方差；按照预设排序规则对多个高斯成分进行重新排序。本实施例中提供了学习更新的一种具体实现方式；即，当存在一个与像素点的基础像素值匹配的高斯成分，说明该高斯成分权重较高，按预设增量增大该高斯成分权值，并更新其样本均差以及样本方差，使得混合高斯背景模型中的高斯成分排布更加合理。In addition, the manner of learning to update specifically includes: comparing the basic pixel value of the pixel point with the plurality of Gaussian components sequentially arranged, and determining whether there is a Gaussian component matching the basic pixel value of the pixel point; when there is one and the pixel When the base pixel value of the point matches the Gaussian component, the weight of the Gaussian component is updated according to the preset weight increment, and the sample mean and the sample variance of the Gaussian component are updated according to the base pixel value of the pixel; Multiple Gaussian components are reordered. In this embodiment, a specific implementation manner of the learning update is provided; that is, when there is a Gaussian component matching the basic pixel value of the pixel, the Gaussian component has a higher weight, and the Gaussian component is increased by a preset increment. Weights, and update their sample mean and sample variance, make the Gaussian component arrangement in the mixed Gaussian background model more reasonable.

另外，在按照预设排序规则对多个高斯成分进行重新排序之前，还包括：当不存在与像素点的基础像素值相匹配的高斯成分时，删除混合高斯模型中排在最后的高斯成分；根据像素点的基础像素值，在混合高斯模型中增加一个新的高斯成分；根据预设的权值减量更新混合高斯模型中新增加的高斯成分以外的高斯成分的权值。本实施例是对上述学习更新方式的进一步完善；即，当不存在与像素点的基础像素值相匹配的高斯成分时，说明需要对混合高斯背景模型中的高斯成分进行更新，此时，增加一个根据该像素点的基础像素值创建的新的高斯成分，并删除一个排在最后的高斯成分，确保了高斯背景模型的准确性。In addition, before reordering the plurality of Gaussian components according to the preset collation, the method further includes: deleting the Gaussian component in the mixed Gaussian model when there is no Gaussian component matching the basic pixel value of the pixel; According to the basic pixel value of the pixel, a new Gaussian component is added to the mixed Gaussian model; the weight of the Gaussian component other than the newly added Gaussian component in the mixed Gaussian model is updated according to the preset weight reduction. This embodiment is a further improvement of the above learning update method; That is, when there is no Gaussian component matching the base pixel value of the pixel point, it is required to update the Gaussian component in the mixed Gaussian background model, and at this time, a new one created according to the base pixel value of the pixel point is added. The Gaussian component and the deletion of a Gaussian component at the end ensure the accuracy of the Gaussian background model.

附图说明DRAWINGS

一个或多个实施例通过与之对应的附图中的图片进行示例性说明，这些示例性说明并不构成对实施例的限定，附图中具有相同参考数字标号的元件表示为类似的元件，除非有特别申明，附图中的图不构成比例限制。The one or more embodiments are exemplified by the accompanying drawings in the accompanying drawings, and FIG. The figures in the drawings do not constitute a scale limitation unless otherwise stated.

图1是根据本发明第一实施方式中的指纹提取方法的具体流程图；1 is a specific flowchart of a fingerprint extraction method according to a first embodiment of the present invention;

图2是根据本发明第二实施方式中的根据每个像素点的感应像素值与预设的每个像素点的混合高斯背景模型，识别每个像素点对应的纹路特征的具体流程图；2 is a specific flowchart of identifying a texture feature corresponding to each pixel point according to a mixed Gaussian background model of each pixel point and a preset mixed Gaussian background model according to a second embodiment of the present invention;

图3是根据本发明第二实施方式中的每个像素点的混合高斯背景模型的预设方式的具体流程图；3 is a specific flowchart of a preset manner of a mixed Gaussian background model of each pixel in the second embodiment of the present invention;

图4是根据本发明第三实施方式中的指纹提取方法的具体流程图；4 is a specific flowchart of a fingerprint extraction method according to a third embodiment of the present invention;

图5是根据本发明第四实施方式的指纹提取装置的方框示意图；Figure 5 is a block diagram showing a fingerprint extracting apparatus according to a fourth embodiment of the present invention;

图6是根据本发明第五实施方式的纹路特征识别模块的方框示意图；6 is a block diagram showing a texture feature recognition module according to a fifth embodiment of the present invention;

图7是根据本发明第六实施方式的指纹提取装置的方框示意图；Figure 7 is a block diagram showing a fingerprint extracting apparatus according to a sixth embodiment of the present invention;

图8是根据本发明第六实施方式的模型预设模块的方框示意图。8 is a block schematic diagram of a model preset module in accordance with a sixth embodiment of the present invention.

具体实施方式detailed description

为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合附图对本发明的各实施方式进行详细的阐述。然而，本领域的普通技术人员可以理解，在本发明各实施方式中，为了使读者更好地理解本申请而提出了许多技术细节。但是，即使没有这些技术细节和基于以下各实施方式的种种变化和修改，也可以实现本申请所要求保护的技术方案。In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the following will be combined with the accompanying drawings. The various embodiments of the present invention are described in detail. However, it will be apparent to those skilled in the art that, in the various embodiments of the present invention, numerous technical details are set forth in order to provide the reader with a better understanding of the present application. However, the technical solutions claimed in the present application can be implemented without these technical details and various changes and modifications based on the following embodiments.

本发明的第一实施方式涉及一种指纹提取方法，应用于智能手机等终端设备。指纹提取方法的具体流程如图1所示。A first embodiment of the present invention relates to a fingerprint extraction method, which is applied to a terminal device such as a smartphone. The specific process of the fingerprint extraction method is shown in Figure 1.

步骤101，判断手指是否触摸。In step 101, it is determined whether the finger is touched.

具体而言，终端设备中的传感器可以检测到是否有手指触摸，传感器可以是压力传感器或者其他可以检测手指按压或触摸的传感器。Specifically, the sensor in the terminal device can detect whether there is a finger touch, and the sensor can be a pressure sensor or other sensor that can detect a finger press or touch.

步骤102，获取指纹感应区域中每个像素点的感应像素值。Step 102: Acquire a sensing pixel value of each pixel in the fingerprint sensing area.

具体而言，当终端设备检测到有手指触摸时，指纹传感器采集此时指纹感应区域中每个像素点对应的感应像素值。更具体的说，指纹传感器采集的数据通常是一个M×N的矩阵，矩阵中每个元素对应指纹传感器采集每个像素点的对应像素值。Specifically, when the terminal device detects that there is a finger touch, the fingerprint sensor collects the sensing pixel value corresponding to each pixel point in the fingerprint sensing area at this time. More specifically, the data collected by the fingerprint sensor is usually an M×N matrix, and each element in the matrix corresponds to a corresponding pixel value of each pixel point collected by the fingerprint sensor.

步骤103，根据每个像素点的感应像素值与预设的每个像素点的混合高斯背景模型，识别每个像素点对应的纹路特征。Step 103: Identify a texture feature corresponding to each pixel point according to the sensed pixel value of each pixel point and a preset mixed Gaussian background model of each pixel point.

具体而言，指纹传感器在手指触摸状态和未触摸状态采集的每个像素点的感应像素值是不同的，预设的每个像素点的混合高斯背景模型是手指未触摸状态下采集到的每个像素点的感应像素值；本实施例中，手指未触摸状态下采集到的每个像素点的感应像素值可以称之为每个像素点对应的基础像素值；其中，每个像素点对应于一个混合高斯背景模型。Specifically, the sensing pixel value of each pixel point collected by the fingerprint sensor in the finger touch state and the untouch state is different, and the preset mixed Gaussian background model of each pixel point is each collected by the finger untouched state. The sensing pixel value of each pixel point in the embodiment may be referred to as the basic pixel value corresponding to each pixel point; wherein each pixel point corresponds to In a mixed Gaussian background model.

据此，对于每个像素点，将手指触摸状态下采集到的感应像素值与混合高斯背景模型进行比较分析，可以获得每个像素点对应的纹路特征。Accordingly, for each pixel point, comparing the sensed pixel values collected under the finger touch state with the mixed Gaussian background model, the texture features corresponding to each pixel point can be obtained.

步骤104，根据每个像素点对应的纹路特征，生成手指的指纹图像。 Step 104: Generate a fingerprint image of the finger according to the texture feature corresponding to each pixel point.

具体而言，获得了每个像素点对的纹路特征之后，将纹路特征按照像素点进行排列，这样就可以获得手指的指纹图像。Specifically, after the texture features of each pixel pair are obtained, the texture features are arranged in accordance with the pixel points, so that the fingerprint image of the finger can be obtained.

如下为进一步具体说明如何获取手指的指纹图像：获得每个像素点对应的纹路特征之后，将凸纹路设为标志位“1”，用颜色深一点的点表示，将凹纹路设为标志位“0”，用颜色浅一点的点表示，这样，便可以将各个像素点的纹路特征用“1”“0”表示，然后用相应颜色的点替换之后，便能得到手指的指纹图像。The following is a detailed description of how to obtain the fingerprint image of the finger: after obtaining the texture feature corresponding to each pixel point, the embossed road is set to the flag bit "1", and the point of the darker color is used to set the embossed road as the flag bit. 0", represented by a lighter dot, so that the texture features of each pixel can be represented by "1" and "0", and then the fingerprint of the finger can be obtained by replacing the dot of the corresponding color.

本发明的第二实施方式涉及一种指纹提取方法，本实施例是对第一实施例的细化，主要细化之处在于：本发明的第二实施方式中，对第一实施方式中步骤103：根据每个像素点的感应像素值与预设的每个像素点的混合高斯背景模型，识别每个像素点对应的纹路特征，进行了具体说明。A second embodiment of the present invention relates to a fingerprint extraction method. This embodiment is a refinement of the first embodiment. The main refinement is that in the second embodiment of the present invention, the steps in the first embodiment are 103: According to the sensing pixel value of each pixel and the preset Gaussian background model of each pixel, the texture feature corresponding to each pixel is identified, and is specifically described.

本实施方式中，根据每个像素点的感应像素值与预设的每个像素点的混合高斯背景模型，识别每个像素点对应的纹路特征的具体流程如图2所示。In this embodiment, according to the sensed pixel value of each pixel point and the preset mixed Gaussian background model of each pixel point, the specific process of identifying the texture feature corresponding to each pixel point is as shown in FIG. 2 .

步骤1031，判断像素点的感应像素值与像素点的混合高斯背景模型是否匹配。若是，进入步骤1032；若否，进入步骤1033。Step 1031: Determine whether the sensed pixel value of the pixel point matches the mixed Gaussian background model of the pixel point. If yes, go to step 1032; if no, go to step 1033.

本步骤中，像素点的混合高斯背景模型的预设方式的具体流程如图3所示。In this step, the specific flow of the preset mode of the mixed Gaussian background model of the pixel points is as shown in FIG. 3.

步骤201，创建像素点的混合高斯模型，混合高斯模型包括依次排列的多个高斯成分。Step 201: Create a mixed Gaussian model of pixel points, and the mixed Gaussian model includes a plurality of Gaussian components arranged in sequence.

于实际中，在指纹传感器采集的像素点的基础像素值中，将某像素点j 的值表示为X_j，可以将X_j表示为由M个高斯成分组成的混合高斯模型，也就是说，该像素点对应的混合高斯模型由M个高斯成分组成，其中，可以用该像素点出现的概率来表示高斯成分，公式如下：In practice, on the basis of the pixel values of the pixels of the fingerprint collected by the sensor, the value of a certain pixel point j is represented as X _{_j,} X _j can be represented by a Gaussian mixture model consisting of M Gaussian components, that is, The mixed Gaussian model corresponding to the pixel is composed of M Gaussian components, wherein the Gaussian component can be represented by the probability of occurrence of the pixel, and the formula is as follows:

其中，ω_k表示混合高斯模型中第k个高斯成分的权重，μ_k和σ_k分别表示第k个高斯成分的均值和标准差，η(X_j,μ_k,σ_k)为高斯概率密度函数，表示为：Where ω _k represents the weight of the kth Gaussian component in the mixed Gaussian model, μ _k and σ _k represent the mean and standard deviation of the kth Gaussian component, respectively, and η(X _j , μ _k , σ _k ) is the Gaussian probability density Function, expressed as:

公式(1)可以表示某时刻下采集的像素j点对应的像素点的基础像素值出现的概率大小。Equation (1) can represent the probability of occurrence of the base pixel value of the pixel corresponding to the pixel j point acquired at a certain moment.

创建像素点的混合高斯模型，首先确立混合高斯模型的高斯分布的数目M(根据传感器数据范围选取，可以为3～5)，为每一个高斯分布初始化一个相同的权重ω_init以及一个较大的标准差σ_init，不同高斯分布的均值统一初始化为第一帧感应数据中对应像素点的像素值。To create a mixed Gaussian model of pixels, first establish the number M of Gaussian distributions of the mixed Gaussian model (selected according to the sensor data range, which can be 3 to 5), and initialize each of the Gaussian distributions with the same weight ω _init and a larger one. The standard deviation σ _init and the mean of the different Gaussian distributions are uniformly initialized to the pixel values of the corresponding pixel points in the first frame sensing data.

步骤202，根据多次获取的像素点的基础像素值，对混合高斯模型进行多次学习更新。Step 202: Perform a plurality of learning updates on the mixed Gaussian model according to the basic pixel values of the plurality of acquired pixels.

于实际中，从第二帧传感器采集的像素点的基础像素值开始进行学习阶段，每个像素对应的不同混合高斯模型的参数不断地被学习更新，整个学习过程中传感器采集1000～2000帧的图像。需要说明的是，本实施方式中对传感器采集图像的帧数不作限制，可以根据传感器采集的像素点的基础像素值的变化波动。In practice, the learning phase is started from the basic pixel value of the pixel collected by the second frame sensor, and the parameters of different mixed Gaussian models corresponding to each pixel are continuously learned and updated, and the sensor collects 1000 to 2000 frames during the whole learning process. image. It should be noted that, in the embodiment, the number of frames of the image collected by the sensor is not limited, and may be fluctuated according to the change of the basic pixel value of the pixel point collected by the sensor.

本实施方式中，学习更新的方式具体包括：In this implementation manner, the manner of learning to update specifically includes:

首先，将像素点的基础像素值与依次排列的多个高斯成分依次比对，判断是否存在一个与像素点的基础像素值匹配的高斯成分。 First, the base pixel value of the pixel is sequentially compared with the plurality of Gaussian components arranged in order, and it is determined whether or not there is a Gaussian component matching the base pixel value of the pixel.

当存在一个与像素点的基础像素值匹配的高斯成分时，根据预设的权值增量更新高斯成分的权值，并根据像素点的基础像素值更新高斯成分的样本均值与样本方差。需要说明的是，像素点的基础像素值为在无手指触摸时获取。When there is a Gaussian component matching the base pixel value of the pixel, the weight of the Gaussian component is updated according to the preset weight increment, and the sample mean and the sample variance of the Gaussian component are updated according to the base pixel value of the pixel. It should be noted that the basic pixel value of the pixel is acquired when there is no finger touch.

于实际中，对于新输入指纹传感器中的像素点的基础像素值，检验每个基础像素值与其对应高斯混合模型中的M个高斯分布是否匹配，如果该基础像素值与其中一高斯成分样本均值的差的绝对值小于第一阈值，则认为基础像素值与该高斯分布匹配。此时，混合高斯模型中的该高斯成分匹配，则该高斯成分需要被更新，按预设权值增量增加权重、用当前基础像素值更新该高斯成分的样本均值与样本方差。而其余高斯成分保持不变。In practice, for the base pixel value of the pixel in the new input fingerprint sensor, it is checked whether each base pixel value matches the M Gaussian distributions in the corresponding Gaussian mixture model, if the base pixel value and one of the Gaussian component samples are averaged The absolute value of the difference is less than the first threshold, and the base pixel value is considered to match the Gaussian distribution. At this time, if the Gaussian component in the mixed Gaussian model matches, the Gaussian component needs to be updated, the weight is increased by the preset weight increment, and the sample mean and the sample variance of the Gaussian component are updated with the current base pixel value. The rest of the Gaussian composition remains unchanged.

本实施例中，用当前基础像素值更新该高斯成分的样本均值的具体方式，可以采用以下公式表示：(1-P)U_k+PX_j；其中，U_k表示更新前的样本均值，X_j表示感应像素值；P表示基础像素值与高斯成分的匹配度。实际上的，匹配度可以基于基础像素值与高斯成分样本均值的差值来计算转化；差值越小，匹配度越高，差值越大，匹配度越小。需要说明的是，用当前像素值更新该高斯成分的样本方差也是基于这一原理进行的，此处不再赘述。In this embodiment, the specific manner of updating the sample mean of the Gaussian component by the current base pixel value may be expressed by the following formula: (1-P)U _k +PX _j ; where U _k represents the sample mean before updating, X _j represents the sensed pixel value; P represents the degree of matching of the base pixel value and the Gaussian component. In fact, the matching degree can be calculated based on the difference between the basic pixel value and the mean value of the Gaussian component sample; the smaller the difference is, the higher the matching degree is, the larger the difference is, and the smaller the matching degree is. It should be noted that updating the sample variance of the Gaussian component with the current pixel value is also based on this principle, and details are not described herein again.

当不存在与像素点的基础像素值相匹配的高斯成分时，删除混合高斯模型中排在最后的高斯成分；根据像素点的基础像素值，在混合高斯模型中增加一个新的高斯成分；根据预设的权值减量更新混合高斯模型中新增加的高斯成分以外的高斯成分的权值。When there is no Gaussian component matching the base pixel value of the pixel, the last Gaussian component in the mixed Gaussian model is deleted; and a new Gaussian component is added to the mixed Gaussian model according to the base pixel value of the pixel; The preset weight decrement updates the weight of the Gaussian component other than the newly added Gaussian component in the mixed Gaussian model.

于实际中，对于新输入指纹传感器中的像素点的基础像素值，如果每个基础像素值与其对应高斯混合模型中的M个高斯分布均不匹配，那么删除混合高斯模型中排名最后的高斯成分，创建新的高斯成分取代之。新创建的高斯成分的均值取该像素点的基础像素值，标准差以及权重用初始化值ω_init和σ_init。其余的高斯成分的均值与方差均不变，而权重按照预设权值减量减小。In practice, for the base pixel value of the pixel in the newly input fingerprint sensor, if each of the base pixel values does not match the M Gaussian distributions in the corresponding Gaussian mixture model, then the last Gaussian component in the mixed Gaussian model is deleted. , create a new Gaussian component to replace it. The mean value of the newly created Gaussian component takes the base pixel value of the pixel, the standard deviation and the weights are initialized with values ω _init and σ _init . The mean and variance of the remaining Gaussian components are unchanged, and the weight is reduced by the preset weight reduction.

然后，在完成了上述对多个高斯成分的更新过程之后，按照预设排序规则对多个高斯成分进行重新排序。其中，预设排序规则遵循权重大、方差小的分量排前面，而权重小、方差大的排后面。Then, after the above updating process for the plurality of Gaussian components is completed, the plurality of Gaussian components are reordered according to a preset sorting rule. Among them, the preset sorting rule follows the component row with large weight and small variance, and the row with small weight and large variance.

步骤203，对多次学习更新后的混合高斯模型中的多个高斯成分的权值进行归一化处理。Step 203: normalize the weights of the plurality of Gaussian components in the mixed Gaussian model after the plurality of learning updates.

具体而言，在多次学习更新后，混合高斯模型中的多个高斯成分的权值发生了变化，混合高斯模型中的多个高斯成分的权值相加可能不等于1(大于1或者小于1)，因此，需对多次更新后的高斯成分的权值进行归一化处理。Specifically, after multiple learning updates, the weights of multiple Gaussian components in the mixed Gaussian model change, and the weights of multiple Gaussian components in the mixed Gaussian model may not be equal to 1 (greater than 1 or less) 1) Therefore, it is necessary to normalize the weights of the Gaussian components after multiple updates.

步骤204，根据预设选取规则，从归一化处理后的多个高斯成分中选取若干个高斯成分，以形成混合高斯背景模型。Step 204: Select a plurality of Gaussian components from the normalized plurality of Gaussian components according to a preset selection rule to form a mixed Gaussian background model.

具体而言，按照排列顺序，选取高斯成分，并累加选取的高斯成分的权值，当累加权值达到预设值时，停止选取。例如，归一化处理后的混合高斯模型包含5个高斯成分，依次排列的5个高斯成分的权值分别为0.35、0.25、0.20、0.15、0.05，若设定的预设值为0.8，而前三个高斯成分的权值累加后达到了预设值，那么即选取前三个高斯成分形成混合高斯背景模型。Specifically, according to the arrangement order, the Gaussian component is selected, and the weight of the selected Gaussian component is accumulated, and when the accumulated weighted value reaches the preset value, the selection is stopped. For example, the normalized mixed Gaussian model includes five Gaussian components, and the weights of the five Gaussian components sequentially arranged are 0.35, 0.25, 0.20, 0.15, and 0.05, respectively, and if the preset value is set to 0.8, The weights of the first three Gaussian components are accumulated and reach the preset value, then the first three Gaussian components are selected to form a mixed Gaussian background model.

需要说明的是，本实施方式对预设值不作限制，可以根据经验或者需求设置；一般而言，设定的预设值小于1(当预设值等于1时，表示选取混合高斯模型中的所有高斯成分形成混合高斯背景模型)。It should be noted that the preset value is not limited in this embodiment, and may be set according to experience or requirement; generally, the preset value is less than 1 (when the preset value is equal to 1, it indicates that the selected Gaussian model is selected. All Gaussian components form a mixed Gaussian background model).

本实施方式中，混合高斯背景模型包含依次排列的若干个高斯成分，当存在一个与像素点的感应像素值匹配的高斯成分时，则认为像素点的感应像素值与像素点的混合高斯背景模型匹配，否则，则认为像素点的感应像素值与像素点的混合高斯背景模型不匹配。In this embodiment, the mixed Gaussian background model includes a plurality of Gaussian components arranged in sequence. When there is a Gaussian component matching the sensed pixel values of the pixel points, the mixed Gaussian background model of the sensed pixel values of the pixel points and the pixel points is considered. Match, otherwise, the sensed pixel value of the pixel is considered to not match the mixed Gaussian background model of the pixel.

本实施方式中，在判断是否存在一个与像素点的感应像素值匹配的高斯成分中，首先，计算像素点的感应像素值与每个高斯成分中的样本均值的差值，并获取绝对值最小的差值作为匹配参数。In this embodiment, it is determined whether there is a Gauss that matches the sensed pixel value of the pixel. In the composition, first, the difference between the sensed pixel value of the pixel point and the sample mean value in each Gaussian component is calculated, and the difference with the smallest absolute value is obtained as the matching parameter.

于实际中，预设第一阈值，当匹配参数小于或等于预设的第一阈值时，判定存在一个与像素点的感应像素值匹配的高斯成分，认为像素点的感应像素值与像素点的混合高斯背景模型匹配；当匹配参数大于第一阈值时，判定不存在与像素点的感应像素值匹配的高斯成分；In practice, the first threshold is preset. When the matching parameter is less than or equal to the preset first threshold, it is determined that there is a Gaussian component matching the sensed pixel value of the pixel, and the sensed pixel value of the pixel is considered to be the pixel point. Mixed Gaussian background model matching; when the matching parameter is greater than the first threshold, determining that there is no Gaussian component matching the sensing pixel value of the pixel;

步骤1032，将像素点的纹路特征识别为凹纹路。In step 1032, the texture feature of the pixel is identified as a concave path.

具体而言，指纹的纹路特征分为凹纹路与凸纹路，手指触摸状态下指纹凹纹路与凸纹路对应的感应值也是不同的，凹纹路对应的像素点的感应像素值与预设的混合高斯背景模型中像素点的感应像素值是匹配的，因此，当判断像素点的感应像素值与像素点的混合高斯背景模型匹配时，则将像素点的纹路特征识别为凹纹路。Specifically, the texture features of the fingerprint are divided into a concave road and a convex road, and the sensing values corresponding to the fingerprint concave road and the convex road in the finger touch state are also different, and the sensing pixel value of the pixel corresponding to the concave road and the preset mixed Gauss The sensed pixel values of the pixel points in the background model are matched. Therefore, when it is determined that the sensed pixel value of the pixel point matches the mixed Gaussian background model of the pixel point, the texture feature of the pixel point is recognized as a concave line.

步骤1033，判断像素点的感应像素值是否满足预设的凸纹路匹配条件。若是，进入步骤1034；若否，进入步骤1032。Step 1033: Determine whether the sensed pixel value of the pixel point satisfies a preset ridge matching condition. If yes, go to step 1034; if no, go to step 1032.

具体而言，在像素点的感应像素值与像素点的混合高斯背景模型不匹配时，如像素点的感应像素值满足预设的凸纹路匹配条件，则将像素点的纹路特征识别为凸纹路，否则，则将像素点的纹路特征识别为凹纹路。Specifically, when the sensed pixel value of the pixel does not match the mixed Gaussian background model of the pixel, if the sensed pixel value of the pixel satisfies a preset ridge matching condition, the texture feature of the pixel is identified as a ridge. Otherwise, the texture feature of the pixel is identified as a concave path.

于实际中，预设第二阈值。当匹配参数大于预设的第二阈值时，则认为像素点的感应像素值满足凸纹路匹配条件。其中，第二阈值大于第一阈值。In practice, the second threshold is preset. When the matching parameter is greater than the preset second threshold, the sensed pixel value of the pixel point is considered to satisfy the ridge matching condition. The second threshold is greater than the first threshold.

步骤1034，将像素点的纹路特征识别为凸纹路。 Step 1034, identifying the texture feature of the pixel as a ridge.

具体而言，如像素点的感应像素值满足凸纹路匹配条件，则将像素点的纹路特征识别为凸纹路。Specifically, if the sensed pixel value of the pixel satisfies the ridge matching condition, the texture feature of the pixel is recognized as a ridge.

本实施方式提供了像素点的混合高斯背景模型的预设方式以及每个像素点对应的纹路特征的具体识别方式，是对第一实施方式的完善说明，以满足实际设计需求。The embodiment provides a preset manner of the mixed Gaussian background model of the pixel points and a specific identification manner of the texture features corresponding to each pixel point, which is a perfect description of the first embodiment, and is full The actual design needs.

本发明的第三实施方式涉及一种指纹提取方法，本实施例是对第一实施例的改进，主要改进之处在于：检测到无手指触摸时，更新每个像素点的混合高斯背景模型。A third embodiment of the present invention relates to a fingerprint extraction method, which is an improvement of the first embodiment, and is mainly improved in that a mixed Gaussian background model of each pixel is updated when no finger touch is detected.

本实施例提供的指纹提取方法的具体流程如图4所示。The specific process of the fingerprint extraction method provided in this embodiment is shown in FIG. 4 .

其中，步骤301与步骤101对应大致相同，步骤303至步骤305与步骤102至步骤104对应大致相同，在此处不再赘述；不同之处在于，本实施例新增加步骤302，具体解释如下：Step 301 and step 101 are substantially the same, and steps 303 to 305 are substantially the same as steps 102 to 104, and are not described here again. The difference is that step 302 is newly added in this embodiment, and the specific explanation is as follows:

步骤302，更新每个像素点的混合高斯背景模型。 Step 302, updating the mixed Gaussian background model of each pixel.

本实施方式中，可以按周期来更新每个像素点的混合高斯背景模型，例如，可以设置白天每四个小时更新每个像素点的混合高斯背景模型。In this embodiment, the mixed Gaussian background model of each pixel point may be updated in cycles. For example, a mixed Gaussian background model of each pixel point may be updated every four hours during the day.

需要说明的是，更新像素点的混合高斯背景模型的过程：首先，根据当前采集的像素点的基础像素值，对混合高斯背景模型进行一次或多次学习更新；然后，对一次或多次学习更新后的混合高斯背景模型中的多个高斯成分的权值进行归一化处理；最后，根据预设选取规则，从归一化处理后的多个高斯成分中选取若干个高斯成分，以形成新的混合高斯背景模型。具体实现过程在第二实施方式中有具体说明，在此不再赘述。It should be noted that the process of updating the mixed Gaussian background model of the pixel points: first, one or more learning updates are performed on the mixed Gaussian background model according to the base pixel values of the currently collected pixel points; and then, one or more learning sessions are performed. The weights of the plurality of Gaussian components in the updated mixed Gaussian background model are normalized; finally, according to the preset selection rules, several Gaussian components are selected from the normalized Gaussian components to form New hybrid Gaussian background model. The specific implementation process is specifically described in the second embodiment, and details are not described herein again.

本实施方式相对第一实施方式而言，在检测到无手指触摸时，更新每个像素点的混合高斯背景模型，及时对像素点的混合高斯背景模型进行更新，以确保环境变化时，像素点的混合高斯背景模型的准确性。Compared with the first embodiment, the present embodiment updates the mixed Gaussian background model of each pixel point when no finger touch is detected, and updates the mixed Gaussian background model of the pixel point in time to ensure that the pixel changes when the environment changes. The accuracy of the mixed Gaussian background model.

本发明第一至第三实施方式中各种方法的步骤划分，只是为了描述清楚，实现时可以合并为一个步骤或者对某些步骤进行拆分，分解为多个步骤，只要包括相同的逻辑关系，都在本专利的保护范围内；对算法中或者流程中添加无关紧要的修改或者引入无关紧要的设计，但不改变其算法和流程的核心设计都在该专利的保护范围内。 The steps of the various methods in the first to third embodiments of the present invention are divided for the sake of clarity of description. The implementation may be combined into one step or split into certain steps and decomposed into multiple steps, as long as the same logical relationship is included. All of them are within the scope of this patent; it is within the scope of this patent to add insignificant modifications to an algorithm or process, or to introduce an insignificant design without changing the core design of its algorithms and processes.

本发明第四实施方式涉及一种指纹提取装置，应用于电子设备，例如手机。如图5所示，指纹提取装置包括：像素值获取模块1、纹路特征识别模块2与指纹图像生成模块3。A fourth embodiment of the present invention relates to a fingerprint extracting apparatus applied to an electronic device such as a mobile phone. As shown in FIG. 5, the fingerprint extraction device includes a pixel value acquisition module 1, a texture feature recognition module 2, and a fingerprint image generation module 3.

像素值获取模块1用于在检测到手指触摸时，获取指纹感应区域中每个像素点的感应像素值；The pixel value obtaining module 1 is configured to acquire, when detecting a finger touch, a sensing pixel value of each pixel in the fingerprint sensing area;

纹路特征识别模块2用于根据每个像素点的感应像素值与预设的每个像素点的混合高斯背景模型，识别每个像素点对应的纹路特征；The texture feature recognition module 2 is configured to identify a texture feature corresponding to each pixel point according to a sensed pixel value of each pixel point and a preset mixed Gaussian background model of each pixel point;

指纹图像生成模块3用于根据每个像素点对应的纹路特征，生成手指的指纹图像。The fingerprint image generating module 3 is configured to generate a fingerprint image of the finger according to the texture feature corresponding to each pixel point.

不难发现，本实施方式为与第一实施方式相对应的装置实施例，本实施方式可与第一实施方式互相配合实施。第一实施方式中提到的相关技术细节在本实施方式中依然有效，为了减少重复，这里不再赘述。相应地，本实施方式中提到的相关技术细节也可应用在第一实施方式中。It is not difficult to find that the present embodiment is an apparatus embodiment corresponding to the first embodiment, and the present embodiment can be implemented in cooperation with the first embodiment. The related technical details mentioned in the first embodiment are still effective in the present embodiment, and are not described herein again in order to reduce repetition. Accordingly, the related art details mentioned in the present embodiment can also be applied to the first embodiment.

本实施方式提供的指纹提取装置与现有技术相比，由获取的手指触摸时指纹感应区域中每个像素点的感应像素值和预设的每个像素点的混合高斯背景模型，然后，识别每个像素点对应的纹路特征，将这些纹路特征按照像素点排列，生成手指的指纹图像；混合高斯背景模型对不稳定的像素具有很好的描述效果，利用混合高斯背景模型提取高质量的指纹图像。Compared with the prior art, the fingerprint extraction device provided by the present embodiment compares the sensing pixel value of each pixel in the fingerprint sensing area and the preset mixed Gaussian background model of each pixel point by the acquired finger, and then identifies The texture features corresponding to each pixel point are arranged according to the pixel points to generate a fingerprint image of the finger; the mixed Gaussian background model has a good description effect on the unstable pixels, and the high-quality fingerprint is extracted by using the mixed Gaussian background model. image.

本发明的第五实施方式涉及一种指纹提取装置，本实施例是对第四实施例的细化，主要细化之处在于：本发明的第五实施方式中，对纹路特征识别模块2所包含的具体模块，进行了进一步的说明。A fifth embodiment of the present invention relates to a fingerprint extracting apparatus. This embodiment is a refinement of the fourth embodiment. The main refinement is that in the fifth embodiment of the present invention, the texture feature identifying module 2 is provided. The specific modules included are further described.

本实施方式中，如图6所示，指纹提取装置的纹路特征识别模块2包括第一匹配单元21、第二匹配单元22。In the present embodiment, as shown in FIG. 6, the texture feature recognition module 2 of the fingerprint extraction device includes a first matching unit 21 and a second matching unit 22.

第一匹配单元21用于判断每个判断像素点的感应像素值与像素点的混合高斯背景模型是否相匹配，当像素点的感应像素值与像素点的混合高斯背景模型相匹配时，第一匹配单元21将像素点的纹路特征识别为凹纹路。The first matching unit 21 is configured to determine whether the sensed pixel value of each of the determined pixel points matches the mixed Gaussian background model of the pixel point, and the mixed pixel value of the pixel point and the mixed Gaussian back of the pixel point When the scene models match, the first matching unit 21 recognizes the texture features of the pixel points as concave lines.

第二匹配单元22用于在第一匹配单元21判定像素点的感应像素值与像素点的混合高斯背景模型不匹配时，判断像素点的感应像素值是否满足预设的凸纹路匹配条件。当像素点的感应像素值满足凸纹路匹配条件时，第二匹配单元22将像素点的纹路特征识别为凸纹路；当像素点的感应像素值不满足凸纹路匹配条件时，第二匹配单元22将像素点的纹路特征识别为凹纹路。The second matching unit 22 is configured to determine, when the first matching unit 21 determines that the sensing pixel value of the pixel point does not match the mixed Gaussian background model of the pixel point, whether the sensing pixel value of the pixel point satisfies a preset ridge matching condition. When the sensed pixel value of the pixel meets the ridge matching condition, the second matching unit 22 recognizes the texture feature of the pixel as a ridge; and when the sensed pixel value of the pixel does not satisfy the ridge matching condition, the second matching unit 22 The texture feature of the pixel is identified as a concave path.

于实际中，混合高斯背景模型包括依次排列的若干个高斯成分，第一匹配单元21具体用于将像素点的感应像素值与若干个高斯成分依次比对，判断是否存在一个与像素点的感应像素值相匹配的高斯成分；当存在一个与像素点的感应像素值相匹配的高斯成分时，第一匹配单元21判定像素点的感应像素值与像素点的混合高斯背景模型相匹配；当不存在与像素点的感应像素值相匹配的高斯成分时，第一匹配单元21判定像素点的感应像素值与像素点的混合高斯背景模型不匹配。In practice, the mixed Gaussian background model includes a plurality of Gaussian components arranged in sequence, and the first matching unit 21 is specifically configured to sequentially compare the sensing pixel values of the pixel points with the plurality of Gaussian components to determine whether there is a sensing of the pixel points. a Gaussian component matching the pixel values; when there is a Gaussian component matching the sensed pixel value of the pixel, the first matching unit 21 determines that the sensed pixel value of the pixel matches the mixed Gaussian background model of the pixel; When there is a Gaussian component matching the sensed pixel value of the pixel, the first matching unit 21 determines that the sensed pixel value of the pixel does not match the mixed Gaussian background model of the pixel.

于实际中，第一匹配单元21包括：计算子单元、判断子单元。In practice, the first matching unit 21 includes: a calculation subunit and a judgment subunit.

其中，计算子单元用于计算像素点的感应像素值与每个高斯成分中的样本均值的差值，并获取绝对值最小的差值作为像素点的感应像素值对应的匹配参数。判断子单元，用于判断匹配参数是否小于或等于预设的第一阈值。The calculation subunit is configured to calculate a difference between the sensed pixel value of the pixel point and the sample mean value in each Gaussian component, and obtain the difference with the smallest absolute value as the matching parameter corresponding to the sensed pixel value of the pixel point. The determining subunit is configured to determine whether the matching parameter is less than or equal to a preset first threshold.

其中，当匹配参数小于或等于预设的第一阈值时，判断子单元判定存在一个与像素点的感应像素值相匹配的高斯成分，当匹配参数大于预设的第一阈值时，判断子单元判定不存在与像素点的感应像素值相匹配的高斯成分。凸纹路匹配条件包括：匹配参数大于预设的第二阈值；其中，第二阈值大于第一阈值。Wherein, when the matching parameter is less than or equal to the preset first threshold, the determining subunit determines that there is a Gaussian component matching the sensing pixel value of the pixel, and when the matching parameter is greater than the preset first threshold, determining the subunit It is determined that there is no Gaussian component that matches the sensed pixel value of the pixel. The ridge matching condition includes: the matching parameter is greater than a preset second threshold; wherein the second threshold is greater than the first threshold.

需要说明的是，本实施方式中对第一阈值和第二阈值不作任何限制，可以根据需要来设置。It should be noted that in the present embodiment, the first threshold and the second threshold are not limited, and may be set as needed.

由于第二实施方式与本实施方式相互对应，因此本实施方式可与第二实施方式互相配合实施。第二实施方式中提到的相关技术细节在本实施方式中依然有效，在第二实施方式中所能达到的技术效果在本实施方式中也同样可以实现，为了减少重复，这里不再赘述。相应地，本实施方式中提到的相关技术细节也可应用在第二实施方式中。Since the second embodiment and the present embodiment correspond to each other, the present embodiment can be combined with the second embodiment. The implementation methods are implemented in cooperation with each other. The related technical details mentioned in the second embodiment are still effective in the present embodiment, and the technical effects that can be achieved in the second embodiment can also be implemented in the present embodiment. To reduce the repetition, details are not described herein again. Accordingly, the related art details mentioned in the present embodiment can also be applied to the second embodiment.

本实施方式提供了纹路特征识别模块的具体组成，可以完成像素点的混合高斯背景模型的预设以及每个像素点对应的纹路特征的识别，是对第四实施方式的完善说明，以满足实际设计需求。The embodiment provides a specific composition of the texture feature recognition module, which can complete the preset of the mixed Gaussian background model of the pixel and the identification of the texture feature corresponding to each pixel, which is a perfect description of the fourth embodiment to meet the actual situation. Design requirements.

本发明第六实施方式涉及一种指纹提取装置，本实施方式是在第四实施方式基础的改进，改进之处在于：请参考图7，指纹提取装置还包括模型预设模块4。A sixth embodiment of the present invention relates to a fingerprint extracting apparatus. The present embodiment is an improvement of the fourth embodiment. The improvement is that, referring to FIG. 7, the fingerprint extracting apparatus further includes a model preset module 4.

如图7所示，模型预设模块4用于在检测到无手指触摸时，更新每个像素点的混合高斯背景模型。As shown in FIG. 7, the model preset module 4 is configured to update the mixed Gaussian background model of each pixel point when no finger touch is detected.

于实际中，如图8，模型预设模块4包括创建单元41、学习更新单元42、归一化处理单元43与选取单元44。In practice, as shown in FIG. 8, the model preset module 4 includes a creation unit 41, a learning update unit 42, a normalization processing unit 43, and a selection unit 44.

创建单元41用于创建像素点的混合高斯模型；混合高斯模型包括依次排列的多个高斯成分；The creating unit 41 is configured to create a mixed Gaussian model of the pixel points; the mixed Gaussian model includes a plurality of Gaussian components arranged in sequence;

学习更新单元42用于根据多次获取的像素点的基础像素值，对混合高斯模型进行多次学习更新；需要说明的是，像素点的基础像素值通过像素值获取模块1在无手指触摸时获取；The learning update unit 42 is configured to perform a plurality of learning updates on the mixed Gaussian model according to the basic pixel values of the pixels that are acquired multiple times. It should be noted that the basic pixel values of the pixel points pass through the pixel value acquiring module 1 when there is no finger touch. Obtain;

归一化处理单元43用于对多次学习更新后的混合高斯模型中的多个高斯成分的权值进行归一化处理；The normalization processing unit 43 is configured to normalize the weights of the plurality of Gaussian components in the mixed Gaussian model after the multiple learning update;

选取单元44，用于根据预设选取规则，从归一化处理后的多个高斯成分中选取若干个高斯成分，以形成混合高斯背景模型。The selecting unit 44 is configured to select a plurality of Gaussian components from the plurality of Gaussian components after the normalization according to the preset selection rule to form a mixed Gaussian background model.

于实际中，学习更新单元42包括匹配子单元、第一更新子单元、排序子单元与第二更新子单元。In practice, the learning update unit 42 includes a matching subunit, a first update subunit, and an ordering. The subunit and the second update subunit.

匹配子单元用于将像素点的基础像素值与依次排列的多个高斯成分依次比对，判断是否存在一个与像素点的基础像素值匹配的高斯成分；The matching sub-unit is configured to sequentially compare the basic pixel value of the pixel with the plurality of Gauss components sequentially arranged, and determine whether there is a Gaussian component matching the basic pixel value of the pixel;

第一更新子单元用于在匹配子单元判定存在一个与像素点的基础像素值匹配的高斯成分时，根据预设的权值增量更新高斯成分的权值，并根据像素点的基础像素值更新高斯成分的样本均值与样本方差；The first update subunit is configured to: when the matching subunit determines that there is a Gaussian component matching the base pixel value of the pixel, update the weight of the Gaussian component according to the preset weight increment, and according to the base pixel value of the pixel Update the sample mean and sample variance of the Gaussian component;

排序子单元用于按照预设排序规则对多个高斯成分进行重新排序；The sorting subunit is configured to reorder a plurality of Gaussian components according to a preset sorting rule;

第二更新子单元用于在匹配子单元判定不存在与像素点的基础像素值匹配的高斯成分时，删除混合高斯模型中排在最后的高斯成分；根据像素点的基础像素值，在混合高斯模型中增加一个新的高斯成分；根据预设的权值减量更新混合高斯模型中新增加的高斯成分以外的高斯成分的权值。The second update subunit is configured to delete the Gaussian component in the mixed Gaussian model when the matching subunit determines that there is no Gaussian component matching the base pixel value of the pixel; according to the base pixel value of the pixel, in the mixed Gaussian A new Gaussian component is added to the model; the weight of the Gaussian component other than the newly added Gaussian component in the mixed Gaussian model is updated according to the preset weight reduction.

由于第三实施方式与本实施方式相互对应，因此本实施方式可与第三实施方式互相配合实施。第三实施方式中提到的相关技术细节在本实施方式中依然有效，在第三实施方式中所能达到的技术效果在本实施方式中也同样可以实现，为了减少重复，这里不再赘述。相应地，本实施方式中提到的相关技术细节也可应用在第三实施方式中。Since the third embodiment and the present embodiment correspond to each other, the present embodiment can be implemented in cooperation with the third embodiment. The technical details mentioned in the third embodiment are still effective in the present embodiment, and the technical effects that can be achieved in the third embodiment are also implemented in the present embodiment. To reduce the repetition, details are not described herein again. Accordingly, the related art details mentioned in the present embodiment can also be applied to the third embodiment.

本实施方式提供的指纹提取装置与第四实施方式相比，在检测到无手指触摸时，更新每个像素点的混合高斯背景模型，及时对像素点的混合高斯背景模型进行更新，以确保环境变化时，像素点的混合高斯背景模型的准确性。Compared with the fourth embodiment, the fingerprint extraction device provided by the embodiment updates the mixed Gaussian background model of each pixel point when the fingerless touch is detected, and updates the mixed Gaussian background model of the pixel point in time to ensure the environment. When changing, the accuracy of the mixed Gaussian background model of the pixel points.

值得一提的是，本发明第四至第六实施方式中所涉及到的各模块均为逻辑模块，在实际应用中，一个逻辑单元可以是一个物理单元，也可以是一个物理单元的一部分，还可以以多个物理单元的组合实现。此外，为了突出本发明的创新部分，本实施方式中并没有将与解决本发明所提出的技术问题关系不太密切的单元引入，但这并不表明本实施方式中不存在其它的单元。It is to be noted that each module involved in the fourth to sixth embodiments of the present invention is a logic module. In practical applications, a logic unit may be a physical unit or a part of a physical unit. It can also be implemented in a combination of multiple physical units. In addition, in order to highlight the innovative part of the present invention, the present embodiment does not introduce a unit that is not closely related to solving the technical problem proposed by the present invention, but this does not mean that there are no other units in the present embodiment.

本领域技术人员可以理解实现上述实施例方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序存储在一个存储介质中，包括若干指令用以使得一个设备(可以是单片机，芯片等)或处理器(processor)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。Those skilled in the art can understand that all or part of the steps in implementing the above embodiments are This may be accomplished by a program instructing associated hardware, the program being stored in a storage medium, including instructions for causing a device (which may be a microcontroller, chip, etc.) or a processor to perform the various embodiments of the present application. All or part of the steps of the method. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

本领域的普通技术人员可以理解，上述各实施方式是实现本发明的具体实施例，而在实际应用中，可以在形式上和细节上对其作各种改变，而不偏离本发明的精神和范围。 A person skilled in the art can understand that the above embodiments are specific embodiments for implementing the present invention, and various changes can be made in the form and details without departing from the spirit and scope of the present invention. range.

Claims

一种指纹提取方法，其特征在于，包括：A fingerprint extraction method, comprising:

当检测到手指触摸时，获取指纹感应区域中每个像素点的感应像素值；When a finger touch is detected, obtaining a sensing pixel value of each pixel in the fingerprint sensing area;

根据每个所述像素点的感应像素值与预设的每个所述像素点的混合高斯背景模型，识别每个所述像素点对应的纹路特征；Identifying a texture feature corresponding to each of the pixel points according to a sensed pixel value of each of the pixel points and a preset mixed Gaussian background model of each of the pixel points;

根据每个所述像素点对应的纹路特征，生成所述手指的指纹图像。A fingerprint image of the finger is generated according to a texture feature corresponding to each of the pixel points.
根据权利要求1所述的指纹提取方法，其特征在于，在所述根据每个所述像素点的感应像素值与预设的每个所述像素点的混合高斯背景模型，识别每个所述像素点对应的纹路特征中，具体包括：The fingerprint extraction method according to claim 1, wherein each of said plurality of said Gaussian background models according to said sensed pixel values of said each pixel point and said preset said pixel point Among the texture features corresponding to the pixel points, specifically include:

对于每个所述像素点，判断所述像素点的感应像素值与所述像素点的混合高斯背景模型是否匹配；For each of the pixel points, determining whether a sensed pixel value of the pixel point matches a mixed Gaussian background model of the pixel point;

当所述像素点的感应像素值与所述像素点的混合高斯背景模型匹配时，将所述像素点的纹路特征识别为凹纹路。When the sensed pixel value of the pixel point matches the mixed Gaussian background model of the pixel point, the texture feature of the pixel point is identified as a concave line.
根据权利要求2所述的指纹提取方法，其特征在于，在所述根据每个所述像素点的感应像素值与预设的每个所述像素点的混合高斯背景模型，识别每个所述像素点对应的纹路特征中，还包括：The fingerprint extraction method according to claim 2, wherein each of said plurality of said Gaussian background models according to said sensed pixel values of said each pixel point and said preset said pixel points Among the texture features corresponding to the pixel points, the method further includes:

当所述像素点的感应像素值与所述像素点的混合高斯背景模型不匹配时，判断所述像素点的感应像素值是否满足预设的凸纹路匹配条件；When the sensed pixel value of the pixel does not match the mixed Gaussian background model of the pixel, determining whether the sensed pixel value of the pixel meets a preset ridge matching condition;

当所述像素点的感应像素值满足所述凸纹路匹配条件时，将所述像素点的纹路特征识别为凸纹路；When the sensed pixel value of the pixel point satisfies the ridge matching condition, the texture feature of the pixel point is identified as a ridge path;

当所述像素点的感应像素值不满足所述凸纹路匹配条件时，将所述像素点的纹路特征识别为所述凹纹路。When the sensed pixel value of the pixel does not satisfy the ridge matching condition, the texture feature of the pixel is identified as the concave path.
根据权利要求3中所述的指纹提取方法，其特征在于，所述混合高斯背景模型包括依次排列的若干个高斯成分；所述判断所述像素点的感应像素值与所述像素点的混合高斯背景模型是否匹配中，具体为：A fingerprint extraction method according to claim 3, wherein said mixture is high The background model includes a plurality of Gaussian components arranged in sequence; and determining whether the sensed pixel value of the pixel is matched with the mixed Gaussian background model of the pixel is specifically:

将所述像素点的感应像素值与所述若干个高斯成分依次比对，判断是否存在一个与所述像素点的感应像素值匹配的高斯成分；And sequentially comparing the sensed pixel value of the pixel with the plurality of Gaussian components to determine whether there is a Gaussian component matching the sensed pixel value of the pixel;

其中，当存在一个与所述像素点的感应像素值匹配的高斯成分时，判定所述像素点的感应像素值与所述像素点的混合高斯背景模型匹配；当不存在与所述像素点的感应像素值相匹配的高斯成分时，判定所述像素点的感应像素值与所述像素点的混合高斯背景模型不匹配。Wherein, when there is a Gaussian component matching the sensed pixel value of the pixel point, determining that the sensed pixel value of the pixel point matches the mixed Gaussian background model of the pixel point; when there is no When the Gaussian component matching the pixel value is sensed, it is determined that the sensed pixel value of the pixel does not match the mixed Gaussian background model of the pixel.
根据权利要求4所述的指纹提取方法，其特征在于，所述将所述像素点的感应像素值与所述若干个高斯成分依次比对，判断是否存在一个与所述像素点的感应像素值匹配的高斯成分中，具体包括：The fingerprint extraction method according to claim 4, wherein the sensing pixel value of the pixel point is sequentially compared with the plurality of Gaussian components, and determining whether there is a sensing pixel value with the pixel point Among the matched Gaussian components, specifically include:

计算所述像素点的感应像素值与每个所述高斯成分中的样本均值的差值，并获取绝对值最小的差值作为匹配参数；Calculating a difference between the sensed pixel value of the pixel point and a sample mean value in each of the Gaussian components, and obtaining a difference having the smallest absolute value as a matching parameter;

判断所述匹配参数是否小于或等于预设的第一阈值；Determining whether the matching parameter is less than or equal to a preset first threshold;

其中，当所述匹配参数小于或等于预设的第一阈值时，判定存在一个与所述像素点的感应像素值匹配的高斯成分；当所述匹配参数大于所述第一阈值时，判定不存在与所述像素点的感应像素值匹配的高斯成分；Wherein, when the matching parameter is less than or equal to the preset first threshold, determining that there is a Gaussian component matching the sensed pixel value of the pixel point; when the matching parameter is greater than the first threshold, determining not There is a Gaussian component that matches the sensed pixel value of the pixel;

所述凸纹路匹配条件包括：所述匹配参数大于预设的第二阈值；所述第二阈值大于所述第一阈值。The ridge matching condition includes: the matching parameter is greater than a preset second threshold; and the second threshold is greater than the first threshold.
根据权利要求1中所述的指纹提取方法，其特征在于，在所述根据每个所述像素点对应的纹路特征，生成所述手指的指纹图像之后，还包括：The fingerprint extraction method according to claim 1, wherein after the generating the fingerprint image of the finger according to the texture feature corresponding to each of the pixel points, the method further includes:

当检测到无手指触摸时，更新每个所述像素点的混合高斯背景模型。When a fingerless touch is detected, a mixed Gaussian background model for each of the pixel points is updated.
根据权利要求1所述的指纹提取方法，其特征在于，所述每个所述像素点的混合高斯背景模型的预设方式，具体包括： The fingerprint extraction method according to claim 1, wherein the preset manner of the mixed Gaussian background model of each of the pixel points comprises:

创建所述像素点的混合高斯模型；所述混合高斯模型包括依次排列的多个高斯成分；Creating a mixed Gaussian model of the pixel points; the mixed Gaussian model comprising a plurality of Gaussian components arranged in sequence;

根据多次获取的所述像素点的基础像素值，对所述混合高斯模型进行多次学习更新；其中，所述像素点的基础像素值为在无手指触摸时获取；Performing a plurality of learning updates on the mixed Gaussian model according to a plurality of acquired base pixel values of the pixel points; wherein, the base pixel value of the pixel points is acquired when there is no finger touch;

对多次学习更新后的所述混合高斯模型中的所述多个高斯成分的权值进行归一化处理；Normalizing the weights of the plurality of Gaussian components in the mixed Gaussian model after the plurality of learning updates;

根据预设选取规则，从归一化处理后的所述多个高斯成分中选取若干个高斯成分，以形成所述混合高斯背景模型。According to a preset selection rule, a plurality of Gaussian components are selected from the plurality of Gaussian components after the normalization to form the mixed Gaussian background model.
根据权利要求7中所述的指纹提取方法，其特征在于，所述学习更新的方式，具体包括：The fingerprint extraction method according to claim 7, wherein the manner of learning to update comprises:

将所述像素点的基础像素值与依次排列的所述多个高斯成分依次比对，判断是否存在一个与所述像素点的基础像素值匹配的高斯成分；And sequentially comparing the basic pixel value of the pixel point with the plurality of Gaussian components sequentially arranged, and determining whether there is a Gaussian component matching the basic pixel value of the pixel point;

当存在一个与所述像素点的基础像素值匹配的高斯成分时，根据预设的权值增量更新所述高斯成分的权值，并根据所述像素点的基础像素值更新所述高斯成分的样本均值与样本方差；When there is a Gaussian component matching the base pixel value of the pixel, the weight of the Gaussian component is updated according to a preset weight increment, and the Gaussian component is updated according to the base pixel value of the pixel Sample mean and sample variance;

按照预设排序规则对所述多个高斯成分进行重新排序。The plurality of Gaussian components are reordered according to a preset collation.
根据权利要求8中所述的指纹提取方法，其特征在于，在所述按照预设排序规则对所述多个高斯成分进行重新排序之前，还包括：The fingerprint extraction method according to claim 8, wherein before the reordering the plurality of Gauss components according to the preset sorting rule, the method further comprises:

当不存在与所述像素点的基础像素值相匹配的高斯成分时，删除所述混合高斯模型中排在最后的高斯成分；Deleting the last Gaussian component of the mixed Gaussian model when there is no Gaussian component matching the base pixel value of the pixel;

根据所述像素点的基础像素值，在所述混合高斯模型中增加一个新的高斯成分；Adding a new Gaussian component to the mixed Gaussian model according to a base pixel value of the pixel;

根据预设的权值减量更新所述混合高斯模型中新增加的所述高斯成分以外的高斯成分的权值。 Updating the weight of the Gaussian component other than the Gaussian component newly added in the mixed Gaussian model according to a preset weight reduction.
一种指纹提取装置，其特征在于，包括：A fingerprint extraction device, comprising:

像素值获取模块，用于在检测到手指触摸时，获取指纹感应区域中每个像素点的感应像素值；a pixel value obtaining module, configured to acquire a sensing pixel value of each pixel in the fingerprint sensing area when a finger touch is detected;

纹路特征识别模块，用于根据每个所述像素点的感应像素值与预设的每个所述像素点的混合高斯背景模型，识别每个所述像素点对应的纹路特征；a texture feature recognition module, configured to identify a texture feature corresponding to each of the pixel points according to a sensed pixel value of each of the pixel points and a preset mixed Gaussian background model of each of the pixel points;

指纹图像生成模块，用于根据每个所述像素点对应的纹路特征，生成所述手指的指纹图像。And a fingerprint image generating module, configured to generate a fingerprint image of the finger according to a texture feature corresponding to each of the pixel points.
根据权利要求10所述的指纹提取装置，其特征在于，所述纹路特征识别模块包括：The fingerprint extraction device according to claim 10, wherein the texture feature recognition module comprises:

第一匹配单元，用于判断每个判断所述像素点的感应像素值与所述像素点的混合高斯背景模型是否相匹配；a first matching unit, configured to determine whether each of the sensing pixel values of the pixel is matched with a mixed Gaussian background model of the pixel;

其中，当所述像素点的感应像素值与所述像素点的混合高斯背景模型相匹配时，所述第一匹配单元将所述像素点的纹路特征识别为凹纹路。Wherein, when the sensed pixel value of the pixel point matches the mixed Gaussian background model of the pixel point, the first matching unit identifies the texture feature of the pixel point as a concave line.
根据权利要求11所述的指纹提取装置，其特征在于，所述纹路特征识别模块还包括：The fingerprint extraction device according to claim 11, wherein the texture feature recognition module further comprises:

第二匹配单元，用于在所述第一匹配单元判定所述像素点的感应像素值与所述像素点的混合高斯背景模型不匹配时，判断所述像素点的感应像素值是否满足预设的凸纹路匹配条件；a second matching unit, configured to determine, when the first matching unit determines that the sensing pixel value of the pixel point does not match the mixed Gaussian background model of the pixel point, determine whether the sensing pixel value of the pixel point satisfies a preset The ridge path matching condition;

其中，当所述像素点的感应像素值满足所述凸纹路匹配条件时，所述第二匹配单元将所述像素点的纹路特征识别为凸纹路；当所述像素点的感应像素值不满足所述凸纹路匹配条件时，所述第二匹配单元将所述像素点的纹路特征识别为所述凹纹路。Wherein, when the sensing pixel value of the pixel point satisfies the ridge matching condition, the second matching unit identifies the texture feature of the pixel point as a ridge path; when the sensing pixel value of the pixel point is not satisfied The second matching unit identifies the texture feature of the pixel point as the concave path when the ridge path matches the condition.
根据权利要求12所述的指纹提取装置，其特征在于，所述混合高斯背景模型包括依次排列的若干个高斯成分； The fingerprint extraction device according to claim 12, wherein the mixed Gaussian background model comprises a plurality of Gaussian components arranged in sequence;

所述第一匹配单元具体用于将所述像素点的感应像素值与所述若干个高斯成分依次比对，判断是否存在一个与所述像素点的感应像素值相匹配的高斯成分；The first matching unit is specifically configured to sequentially compare the sensing pixel value of the pixel point with the plurality of Gaussian components, and determine whether there is a Gaussian component matching the sensing pixel value of the pixel point;

其中，当存在一个与所述像素点的感应像素值相匹配的高斯成分时，所述第一匹配单元判定所述像素点的感应像素值与所述像素点的混合高斯背景模型相匹配；当不存在与所述像素点的感应像素值相匹配的高斯成分时，所述第一匹配单元判定所述像素点的感应像素值与所述像素点的混合高斯背景模型不匹配。Wherein, when there is a Gaussian component matching the sensed pixel value of the pixel point, the first matching unit determines that the sensed pixel value of the pixel point matches the mixed Gaussian background model of the pixel point; When there is no Gaussian component matching the sensed pixel value of the pixel, the first matching unit determines that the sensed pixel value of the pixel does not match the mixed Gaussian background model of the pixel.
根据权利要求13所述的指纹提取装置，其特征在于，所述第一匹配单元包括：The fingerprint extraction device according to claim 13, wherein the first matching unit comprises:

计算子单元，用于计算所述像素点的感应像素值与每个所述高斯成分中的样本均值的差值，并获取绝对值最小的差值作为所述像素点的感应像素值对应的匹配参数；a calculating subunit, configured to calculate a difference between the sensing pixel value of the pixel point and a sample mean value in each of the Gaussian components, and obtain a difference with a minimum absolute value as a matching corresponding to the sensing pixel value of the pixel point parameter;

判断子单元，用于判断所述匹配参数是否小于或等于预设的第一阈值；a determining subunit, configured to determine whether the matching parameter is less than or equal to a preset first threshold;

其中，当所述匹配参数小于或等于预设的第一阈值时，所述判断子单元判定存在一个与所述像素点的感应像素值相匹配的高斯成分；当所述匹配参数大于预设的第一阈值时，所述判断子单元判定不存在与所述像素点的感应像素值相匹配的高斯成分；Wherein, when the matching parameter is less than or equal to a preset first threshold, the determining subunit determines that there is a Gaussian component matching the sensing pixel value of the pixel; when the matching parameter is greater than a preset When the first threshold is reached, the determining subunit determines that there is no Gaussian component matching the sensed pixel value of the pixel point;

所述凸纹路匹配条件包括：所述像素点的感应像素值大于预设的第二阈值；所述第二阈值大于所述第一阈值。The ridge matching condition includes: the sensing pixel value of the pixel point is greater than a preset second threshold; and the second threshold is greater than the first threshold.
根据权利要求10所述的指纹提取装置，其特征在于，所述指纹提取装置还包括：The fingerprint extraction device according to claim 10, wherein the fingerprint extraction device further comprises:

模型预设模块，用于在检测到无手指触摸时，更新每个所述像素点的混合高斯背景模型。 A model preset module is configured to update a mixed Gaussian background model of each of the pixels when a fingerless touch is detected.
根据权利要求15所述的指纹提取装置，其特征在于，所述模型预设模块包括：The fingerprint extraction device according to claim 15, wherein the model preset module comprises:

创建单元，用于创建所述像素点的混合高斯模型；所述混合高斯模型包括依次排列的多个高斯成分；Creating a unit for creating a mixed Gaussian model of the pixel points; the mixed Gaussian model comprising a plurality of Gaussian components arranged in sequence;

学习更新单元，用于根据多次获取的所述像素点的基础像素值，对所述混合高斯模型进行多次学习更新；其中，所述像素点的基础像素值通过像素值获取模块在无手指触摸时获取；a learning update unit, configured to perform a plurality of learning updates on the mixed Gaussian model according to the base pixel value of the pixel point acquired multiple times; wherein the basic pixel value of the pixel point passes through the pixel value acquiring module without a finger Get when touched;

归一化处理单元，用于对多次学习更新后的所述混合高斯模型中的所述多个高斯成分的权值进行归一化处理；a normalization processing unit, configured to perform normalization processing on weights of the plurality of Gaussian components in the mixed Gaussian model after multiple learning updates;

选取单元，用于根据预设选取规则，从归一化处理后的所述多个高斯成分中选取若干个高斯成分，以形成所述混合高斯背景模型。And a selecting unit, configured to select a plurality of Gaussian components from the plurality of Gaussian components after the normalization according to a preset selection rule to form the mixed Gaussian background model.
根据权利要求16所述的指纹提取装置，其特征在于，所述学习更新单元具体包括：The fingerprint extraction device according to claim 16, wherein the learning update unit specifically comprises:

匹配子单元，用于将所述像素点的基础像素值与依次排列的所述多个高斯成分依次比对，判断是否存在一个与所述像素点的基础像素值匹配的高斯成分；a matching sub-unit, configured to sequentially compare the basic pixel value of the pixel point with the plurality of Gaussian components sequentially arranged, and determine whether there is a Gaussian component matching the basic pixel value of the pixel point;

第一更新子单元，用于在所述匹配子单元判定存在一个与所述像素点的基础像素值匹配的高斯成分时，根据预设的权值增量更新所述高斯成分的权值，并根据所述像素点的基础像素值更新所述高斯成分的样本均值与样本方差；a first update subunit, configured to: when the matching subunit determines that there is a Gaussian component matching the base pixel value of the pixel, update the weight of the Gaussian component according to a preset weight increment, and Updating a sample mean and a sample variance of the Gaussian component according to a base pixel value of the pixel point;

排序子单元，用于按照预设排序规则对所述多个高斯成分进行重新排序。a sorting subunit for reordering the plurality of Gaussian components according to a preset collation.
根据权利要求17所述的指纹提取装置，其特征在于，所述学习更新单元还包括： The fingerprint extraction device according to claim 17, wherein the learning update unit further comprises:

第二更新子单元，用于在所述匹配子单元判定不存在与所述像素点的基础像素值匹配的高斯成分时，删除所述混合高斯模型中排在最后的高斯成分；根据所述像素点的基础像素值，在所述混合高斯模型中增加一个新的高斯成分；根据预设的权值减量更新所述混合高斯模型中新增加的所述高斯成分以外的高斯成分的权值。 a second update subunit, configured to delete a Gaussian component in the mixed Gaussian model when the matching subunit determines that there is no Gaussian component matching the base pixel value of the pixel; a base pixel value of the point, adding a new Gaussian component to the mixed Gaussian model; updating a weight of a Gaussian component other than the Gaussian component newly added in the mixed Gaussian model according to a preset weight decrement.