WO2020210954A1 - Procédé et appareil d'étalonnage d'image et dispositif électronique - Google Patents

Procédé et appareil d'étalonnage d'image et dispositif électronique Download PDF

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Publication number
WO2020210954A1
WO2020210954A1 PCT/CN2019/082739 CN2019082739W WO2020210954A1 WO 2020210954 A1 WO2020210954 A1 WO 2020210954A1 CN 2019082739 W CN2019082739 W CN 2019082739W WO 2020210954 A1 WO2020210954 A1 WO 2020210954A1
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WIPO (PCT)
Prior art keywords
calibration
iterative learning
image
original image
calibration parameter
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PCT/CN2019/082739
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English (en)
Chinese (zh)
Inventor
程雷刚
Original Assignee
深圳市汇顶科技股份有限公司
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Application filed by 深圳市汇顶科技股份有限公司 filed Critical 深圳市汇顶科技股份有限公司
Priority to PCT/CN2019/082739 priority Critical patent/WO2020210954A1/fr
Priority to CN201980000591.3A priority patent/CN110192201B/zh
Publication of WO2020210954A1 publication Critical patent/WO2020210954A1/fr

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    • 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
    • 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
    • G06V10/30—Noise filtering
    • 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
    • G06V40/12—Fingerprints or palmprints
    • 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
    • G06V40/12—Fingerprints or palmprints
    • G06V40/1347—Preprocessing; Feature extraction

Definitions

  • the embodiments of the present application relate to the field of electronics, and more specifically, to methods, devices, and electronic equipment for calibrating images.
  • the signal carrier carrying fingerprint information will be disturbed when it penetrates the obstacle.
  • the quality of the fingerprint image will be further affected.
  • a method, device and electronic equipment for calibrating images are provided, which can effectively calibrate images.
  • a method for calibrating an image including:
  • the calibration parameters obtained after iterative learning of the i+1th original image in the n original images are based on the calibration parameters obtained after iterative learning of the i-th original image in the n original images and the calibration parameters
  • the parameter determined by the pixel value of the i+1th original image, the first calibration parameter is the calibration parameter of the nth original image in the n original images after iterative learning, and n is the first preset value And it is a positive integer, 1 ⁇ i ⁇ n;
  • the target image is calibrated based on the first calibration parameter.
  • an apparatus for calibrating an image including:
  • a determining unit configured to determine the first calibration parameter through iterative learning of n original images
  • the calibration parameters obtained after iterative learning of the i+1th original image in the n original images are based on the calibration parameters obtained after iterative learning of the i-th original image in the n original images and the calibration parameters
  • the parameter determined by the pixel value of the i+1th original image, the first calibration parameter is the calibration parameter of the nth original image in the n original images after iterative learning, and n is the first preset value And it is a positive integer, 1 ⁇ i ⁇ n;
  • the calibration unit is configured to calibrate the target image based on the first calibration parameter.
  • an electronic device including:
  • a fingerprint module is arranged on the surface or inside of the electronic device;
  • the fingerprint module is electrically connected to the device
  • the fingerprint module is configured to receive a fingerprint detection signal returned by reflection or scattering of a human finger above the display screen, and the fingerprint detection signal carries fingerprint information of the finger.
  • the method, device and electronic device for calibrating images in this application are embodiments. They can not rely on or rely on a small amount of prior information, and update the fingerprint image for calibration by continuously learning the original image in the user application process.
  • the calibration parameters can not only simplify the operation process, but also effectively improve the calibration accuracy.
  • the method, device and electronic equipment can filter out the image used to calibrate the fingerprint image from the original image in the user application process (for example, the fingerprint identification process), and the technical solution of the present application can be applied to relatively harsh Under the scenario, it has a wider application scenario and higher fingerprint recognition performance.
  • the electronic device selects original images without abnormalities in the collected original images for iterative learning, or cuts out abnormal parts in the original images, and only performs iterative learning on some areas, which can effectively avoid the occurrence of The effect of the abnormal part of the abnormal original image on the iterative learning, thereby improving the accuracy of the calibration parameters.
  • Fig. 1 is a schematic plan view of an electronic device with a collection area in a display screen to which the present application can be applied.
  • Fig. 2 is a schematic partial cross-sectional view of the electronic device shown in Fig. 1 along A'-A'.
  • Fig. 3 is another schematic structural diagram of an electronic device according to an embodiment of the present application.
  • Fig. 4 is a schematic flowchart of a method for calibrating an image according to an embodiment of the present application.
  • FIG. 5 is another schematic flowchart of a method for calibrating an image according to an embodiment of the present application.
  • Fig. 6 is a schematic block diagram of an apparatus for calibrating an image according to an embodiment of the present application.
  • FIG. 7 is another schematic block diagram of the apparatus for calibrating an image according to an embodiment of the present application.
  • Fig. 8 is another schematic block diagram of an electronic device according to an embodiment of the present application.
  • portable or mobile computing devices such as smartphones, notebook computers, tablet computers, and gaming devices, as well as other electronic devices such as electronic databases, automobiles, and bank automated teller machines (ATM).
  • ATM bank automated teller machines
  • the embodiments of the present application are not limited thereto.
  • biometric technology includes but is not limited to fingerprint recognition, palmprint recognition, iris recognition, face recognition, and living body recognition.
  • biometric identification technology may be a capacitive, optical, ultrasonic or other biometric identification technology.
  • optical fingerprint recognition technology is an example to describe the application scenarios of the embodiments of the present application.
  • Optical fingerprint recognition technology can be used for under-screen fingerprint recognition technology and in-screen fingerprint recognition technology.
  • the under-screen fingerprint recognition technology refers to the installation of the fingerprint recognition module below the display screen, so as to realize the fingerprint recognition operation in the display area of the display screen. There is no need to set a fingerprint collection area on the front of the electronic device except for the display area.
  • the fingerprint recognition module uses light returned from the top surface of the display assembly of the electronic device or an externally added invisible LED light source (such as an infrared LED light source) to perform fingerprint sensing and other sensing operations.
  • This returned light carries information about objects (for example, fingers) in contact with the top surface of the display assembly, and the fingerprint recognition module located below the display assembly collects and detects this returned light to realize fingerprint recognition under the screen.
  • the fingerprint recognition module can be designed to achieve desired optical imaging by appropriately configuring optical elements for collecting and detecting the returned light.
  • in-display fingerprint recognition technology refers to the installation of fingerprint recognition modules or part of fingerprint recognition modules inside the display screen, so as to realize fingerprint recognition operations in the display area of the display screen without the need for electronic
  • the fingerprint collection area is set on the front of the device except the display area.
  • FIG. 1 and 2 show schematic diagrams of an electronic device 100 to which under-screen fingerprint recognition technology can be applied.
  • FIG. 1 is a front schematic diagram of the electronic device 100
  • FIG. 2 is a partial cross-sectional structure diagram of the electronic device 100 shown in FIG.
  • the electronic device 100 may include a display screen 120 and a fingerprint recognition module 140.
  • the display screen 120 may be a self-luminous display, which uses a self-luminous display unit as display pixels.
  • the display screen 120 may be an Organic Light-Emitting Diode (OLED) display screen or a Micro-LED (Micro-LED) display screen.
  • the display screen 120 may also be a liquid crystal display (LCD) or other passive light-emitting display, which is not limited in the embodiment of the present application.
  • the display screen 120 may also be specifically a touch display screen, which can not only perform screen display, but also detect a user's touch or pressing operation, thereby providing a user with a human-computer interaction interface.
  • the electronic device 100 may include a touch sensor, and the touch sensor may specifically be a touch panel (TP), which may be provided on the surface of the display screen 120, or may be partially integrated or The whole is integrated into the display screen 120 to form the touch display screen.
  • TP touch panel
  • the fingerprint recognition module 140 may be an optical fingerprint recognition module, such as an optical fingerprint sensor.
  • the fingerprint identification module 140 may include a fingerprint sensor chip with an optical sensing array (hereinafter also referred to as an optical fingerprint sensor).
  • the optical sensing array includes multiple optical sensing units, and each optical sensing unit may specifically include a photodetector or a photoelectric sensor.
  • the fingerprint identification module 140 may include a photodetector array (or called a photodetector array, a photodetector array), which includes a plurality of photodetectors distributed in an array.
  • the fingerprint recognition module 140 may be arranged in a partial area below the display screen 120, so that the fingerprint collection area (or detection area) 130 of the fingerprint recognition module 140 is at least partially located on the display screen 120. ⁇ display area 102.
  • the fingerprint identification module 140 can also be arranged in other positions, such as the side of the display screen 120 or the non-transparent area of the edge of the electronic device 100.
  • the optical signal of at least part of the display area of the display screen 120 can be guided to the fingerprint recognition module 140 through the optical path design, so that the fingerprint collection area 130 is actually located in the display area of the display screen 120 .
  • the fingerprint recognition module 140 may include only one fingerprint sensor chip. At this time, the fingerprint collection area 130 of the fingerprint recognition module 140 has a small area and a fixed position. Therefore, the user needs to input fingerprints. Press the finger to a specific position of the fingerprint collection area 130, otherwise the fingerprint recognition module 140 may not be able to collect the fingerprint image, resulting in poor user experience.
  • the fingerprint identification module 140 may specifically include a plurality of fingerprint sensor chips; the plurality of fingerprint sensor chips may be arranged side by side under the display screen 120 in a splicing manner, and the plurality of fingerprint sensor chips The sensing areas of the two fingerprint sensor chips together constitute the fingerprint collection area 130 of the fingerprint identification module 140.
  • the fingerprint collection area 130 of the fingerprint identification module 140 may include multiple sub-areas, and each sub-area corresponds to the sensing area of one of the fingerprint sensor chips, so that the fingerprint of the optical fingerprint module 130 is collected
  • the area 130 can be extended to the main area of the lower half of the display screen, that is, to the area where the finger is habitually pressed, so as to realize the blind fingerprint input operation.
  • the fingerprint detection area 130 can also be extended to half of the display area or even the entire display area, thereby realizing half-screen or full-screen fingerprint detection.
  • the multiple fingerprint sensor chips may be individually packaged fingerprint sensor chips, or multiple chips (Die) packaged in the same chip package.
  • the multiple fingerprint sensor chips can also be fabricated on different regions of the same chip (Die) through a semiconductor process.
  • the area or light sensing range of the optical sensing array of the fingerprint identification module 140 corresponds to the fingerprint collection area 130 of the fingerprint identification module 140.
  • the fingerprint collection area 130 of the fingerprint recognition module 140 may be equal to or not equal to the area or the light sensing range of the optical sensing array of the fingerprint recognition module 140, which is not specifically limited in the embodiment of the present application.
  • the fingerprint collection area 130 of the fingerprint identification module 140 can be designed to be substantially the same as the area of the sensing array of the fingerprint identification module 140.
  • the area of the fingerprint collection area 130 of the fingerprint recognition module 140 can be larger than the area of the fingerprint recognition module 140 sensing array through the design of the light path of convergent light or the design of the light path of reflected light.
  • the optical path design of the fingerprint identification module 140 is exemplified below.
  • the optical collimator may be specifically a collimator layer made on a semiconductor silicon wafer. , It has a plurality of collimating units or micro-holes.
  • the collimating unit may be specifically a small hole.
  • the reflected light reflected from the finger the light that is perpendicularly incident on the collimating unit can pass through and be
  • the fingerprint sensor chip receives, and the light whose incident angle is too large is attenuated by multiple reflections inside the collimating unit. Therefore, each fingerprint sensor chip can basically only receive the reflected light reflected by the fingerprint lines directly above it. It can effectively improve the image resolution, thereby improving the fingerprint recognition effect.
  • a collimating unit may be configured for one optical sensor unit in the optical sensor array of each fingerprint sensor chip, and the collimating unit may be attached to the corresponding optical sensor.
  • the multiple optical sensing units can also share one collimating unit, that is, the one collimating unit has an aperture large enough to cover the multiple optical sensing units. Since one collimating unit can correspond to multiple optical sensing units, the correspondence between the spatial period of the display screen 120 and the spatial period of the fingerprint sensor chip is destroyed.
  • the spatial structure of the light-emitting display array of the display screen 120 and the fingerprint sensor chip are The spatial structure of the optical sensor array is similar, and it can also effectively prevent the fingerprint identification module 140 from using the light signal passing through the display screen 120 to perform fingerprint imaging to generate moiré fringes, which effectively improves the fingerprint identification effect of the fingerprint identification module 140.
  • the optical lens may include an optical lens (Lens) layer, which has one or more lens units, such as one or more aspheric lenses.
  • the lens group is used to converge the reflected light reflected from the finger to the sensing array of the fingerprint sensor chip below it, so that the sensing array can perform imaging based on the reflected light, thereby acquiring the fingerprint image of the finger.
  • the optical lens layer may also be formed with a pinhole in the optical path of the lens unit, and the pinhole may cooperate with the optical lens layer to expand the field of view of the fingerprint recognition module 140 to improve the fingerprint recognition module 140 Fingerprint imaging effect.
  • each fingerprint sensor chip may be configured with an optical lens for fingerprint imaging, or multiple fingerprint sensor chips may be configured with an optical lens to achieve light convergence and fingerprint imaging.
  • the fingerprint sensor chip can also be equipped with two or more optical lenses to cooperate with the two sensors. The array or multiple sensing arrays perform optical imaging, thereby reducing the imaging distance and enhancing the imaging effect.
  • the micro-lens layer may have a micro-lens array formed by a plurality of micro-lenses, which may be obtained through a semiconductor growth process or other The process is formed above the sensing array of the fingerprint sensor chip, and each microlens can correspond to one of the sensing units of the sensing array.
  • Other optical film layers may be formed between the microlens layer and the sensing unit, such as a dielectric layer or a passivation layer. More specifically, the microlens layer and the sensing unit may also include micropores.
  • the light blocking layer wherein the micro holes are formed between the corresponding micro lens and the sensing unit, the light blocking layer can block the optical interference between the adjacent micro lens and the sensing unit, and allow light to pass through the micro lens
  • the lens is converged into the microhole and is transmitted to the sensing unit corresponding to the microlens through the microhole to perform optical fingerprint imaging.
  • a microlens layer can be further provided under the collimator layer or the optical lens layer.
  • the collimator layer or the optical lens layer is used in combination with the micro lens layer, its specific laminated structure or optical path may need to be adjusted according to actual needs.
  • the fingerprint identification module 140 can be used to collect user fingerprint information (such as fingerprint image information).
  • the display screen 120 can adopt a display screen with a self-luminous display unit, such as an organic light-emitting diode (Organic Light-Emitting Diode, OLED) display or a micro-LED (Micro-LED) display Screen.
  • the fingerprint recognition module 140 can use the display unit (ie, the OLED light source) of the OLED display screen located in the fingerprint collection area 130 as the excitation light source for optical fingerprint detection.
  • the display screen 120 When a finger touches, presses, or approaches (for ease of description, collectively referred to as pressing in this application) in the fingerprint collection area 130, the display screen 120 emits a beam of light to the finger above the fingerprint collection area 130. The surface is reflected to form reflected light or is scattered inside the finger to form scattered light. In related patent applications, for ease of description, the above-mentioned reflected light and scattered light are collectively referred to as reflected light. Because the ridge and valley of the fingerprint have different light reflection capabilities, the reflected light from the fingerprint ridge and the fingerprint ridge have different light intensities. After the reflected light passes through the display screen 120, it is affected by the fingerprint.
  • the fingerprint sensor chip in the identification module 140 receives and converts it into a corresponding electrical signal, that is, a fingerprint detection signal; fingerprint image data can be obtained based on the fingerprint detection signal, and fingerprint matching verification can be further performed, so that the electronic The device 100 implements an optical fingerprint recognition function.
  • the electronic device 100 adopting the above structure does not need to reserve a special space on the front of the fingerprint button (such as the Home button), so a full screen solution can be adopted. Therefore, the display area 102 of the display screen 120 can be substantially extended to the entire front surface of the electronic device 100.
  • the fingerprint identification module 140 may also use a built-in light source or an external light source to provide an optical signal for fingerprint detection and identification.
  • the fingerprint identification module 140 can be applied not only to self-luminous displays such as OLED displays, but also to non-self-luminous displays, such as liquid crystal displays or other passive light-emitting displays.
  • the optical fingerprint system of the electronic device 100 may also include an excitation light source for optical fingerprint detection.
  • the light source may specifically be an infrared light source or a light source of non-visible light of a specific wavelength, which may be arranged under the backlight module of the liquid crystal display or in the edge area under the protective cover of the electronic device 100, and the fingerprint recognition module 140 may
  • the liquid crystal panel or the protective cover is arranged under the edge area and guided by the light path so that the fingerprint detection light can reach the fingerprint identification module 140; or, the fingerprint identification module 140 can also be arranged under the backlight module, and
  • the backlight module is designed to allow the fingerprint detection light to pass through the liquid crystal panel and the backlight module and reach the fingerprint recognition module 140 by opening holes or other optical designs on the film layers such as the diffusion sheet, the brightness enhancement sheet, and the reflection sheet.
  • the fingerprint identification module 140 adopts a built-in light source or
  • the electronic device 100 may further include a protective cover 110.
  • the cover 110 may be specifically a transparent cover, such as a glass cover or a sapphire cover, which is located above the display screen 120 and covers the front of the electronic device 100, and the surface of the cover 110 may also be provided with a protective layer. Therefore, in the embodiment of the present application, the so-called finger pressing the display screen 120 may actually refer to the finger pressing the cover 110 above the display 120 or covering the surface of the protective layer of the cover 110.
  • a circuit board 150 such as a flexible printed circuit (FPC) (Flexible Printed Circuit, FPC), may also be provided under the fingerprint identification module 140.
  • FPC Flexible Printed Circuit
  • the fingerprint recognition module 140 can be soldered to the circuit board 150 through pads, and realize electrical interconnection and signal transmission with other peripheral circuits or other components of the electronic device 100 through the circuit board 150.
  • the fingerprint recognition module 140 can receive the control signal of the processing unit of the electronic device 100 through the circuit board 150, and can also output the fingerprint detection signal from the fingerprint recognition module 140 to the processing unit of the electronic device 100 through the circuit board 150. Control unit, etc.
  • Fig. 3 is another schematic structural diagram of an electronic device according to an embodiment of the present application.
  • the electronic device 300 may include a display screen 220 and an optical fingerprint sensor 230.
  • the fingerprint detection signal carrying fingerprint information can penetrate the display screen 220 to reach the upper surface of the optical fingerprint sensor 230, so that the optical fingerprint sensor 230 performs fingerprint imaging based on the fingerprint detection signal, and then performs fingerprint recognition.
  • the display screen 220 may include a cover glass 221, a first adhesive layer 222, a polarizer 223, a second adhesive layer 224, and a touch panel (TP) layer 225 from top to bottom.
  • the sealing glass 226 is used for sealing.
  • the substrate glass 228, the sealing glass 226, and the display pixel layer 227 between them cooperate with the display driving circuit to realize the display function.
  • the TP layer 225 above the sealing glass 226 cooperates with the touch driving circuit to realize the touch function.
  • the TP layer 225 may be etched into various patterns.
  • the polarizer 223 is disposed on the TP layer 225 through the second adhesive layer 224, and the polarizer 223 can be used to suppress the reflection of the display screen 220 to ambient light, thereby achieving a higher display contrast.
  • the cover glass 221 is disposed on the polarizer 223 through the first adhesive layer 221 to protect the display screen 220.
  • the optical fingerprint sensor 230 is placed or attached to the bottom of the substrate glass 228, so that the under-screen optical fingerprint recognition can be realized locally or in the full screen in the display area of the display screen.
  • the fingerprint detection signal carrying fingerprint information will be interfered when passing through the display screen 220, thereby affecting the fingerprint image. Image quality.
  • the quality of the fingerprint image will be further affected.
  • This application proposes a method for calibrating an image, which can not rely on prior information or rely on a small amount of prior information.
  • the calibration parameters used to calibrate the fingerprint image can be updated, which can not only simplify The operation process can also effectively improve the calibration accuracy.
  • the technical solution of the present application can be applied to more severe scenarios, making it have a broader application scenario and higher Fingerprint recognition performance.
  • FIG. 4 is a schematic flowchart of a method 300 for calibrating an image according to an embodiment of the present application. It should be understood that the method 300 may be executed by any electronic device with image processing capabilities. For ease of understanding, the method 300 is described below by taking an electronic device as an example.
  • the method 300 may include:
  • S310 The electronic device determines the first calibration parameter through iterative learning of n original images.
  • the calibration parameters obtained after iterative learning of the i+1th original image in the n original images are based on the calibration parameters obtained after iterative learning of the i-th original image in the n original images and the calibration parameters
  • the parameter determined by the pixel value of the i+1th original image, the first calibration parameter is the calibration parameter of the nth original image in the n original images after iterative learning, and n is the first preset value And it is a positive integer, 1 ⁇ i ⁇ n;
  • S320 The electronic device calibrates the target image based on the first calibration parameter.
  • the electronic device after the electronic device acquires the first calibration parameter through iterative learning of n original images, it can calibrate subsequent original images based on the first calibration parameter. For example, suppose the target image is the n+jth original image, and j is a positive integer. When j is 1, the electronic device may directly calibrate the n+1th original image based on the first calibration parameter.
  • the electronic device can directly calibrate the n+jth original image based on the first calibration parameter, or it can be based on the first calibration parameter by comparing the n+1th to n
  • the iterative learning of part or all of the original images in the +j original images first obtains the second calibration parameter, and then calibrates the n+j+1th original image based on the second calibration parameter.
  • the abnormal original image or the abnormal part in the original image may not be used for iterative learning. Specifically, after the electronic device acquires the original image, it first determines whether the acquired original image is abnormal. If there is an abnormality, it is not used for iterative learning; or the abnormal part of the abnormal image is cut off, and the normal part is used for local Regional iterative learning; if there is no abnormality, it is used for iterative learning.
  • the electronic device may determine the first calibration parameter by iterative learning on data other than the abnormal data in the n original images. Calibration parameter; or the electronic device may determine the first calibration parameter through iterative learning of images other than abnormal images in the n original images.
  • the abnormal image may be an original image with abnormal data.
  • the abnormal data includes but is not limited to: incomplete data caused by a part of the user's finger not pressing the electronic device and abnormal data caused by strong light.
  • the electronic device screens out the image used to calibrate the fingerprint image from the original image in the user application process (for example, the fingerprint recognition process), so that the technical solution of this application can be applied to more severe scenarios. , Making it have a wider application scenario and higher fingerprint recognition performance.
  • the electronic device can effectively avoid iterative learning of abnormal original images by selecting original images without abnormalities or normal parts of abnormal images from the collected original images for iterative learning, thereby improving the calibration parameters. Accuracy.
  • the calibration parameter obtained after the i-th original image undergoes iterative learning may correspond to the first weight value, and the pixel value of the i+1-th original image corresponds to the second weight value.
  • the first weight value The sum of the second weight value is 1.
  • the second weight value gradually decreases to a constant value as the number of the original images used for iterative learning increases.
  • FIG. 4 is another schematic flowchart of a method 300 for calibrating an image according to an embodiment of the present application.
  • the method 300 may include:
  • the electronic device initializes the calibration parameters. For example, the electronic device initializes the calibration parameter to zero.
  • the electronic device collects original images and counts only the original images that are learned. Specifically, after the electronic device has learned the m-1 original images, it collects the m-th original image.
  • the electronic device determines whether the number (m-1) of original images that have undergone iterative learning is greater than a first preset value, thereby determining whether the calibration parameters obtained after the iterative learning of the m-1 original images can be used Calibrate the image. Specifically, when m-1 is greater than or equal to the first preset value, the electronic device determines that the calibration parameters obtained after iterative learning of the m-1 original images can be used to calibrate the image, when m-1 When it is less than the first preset value, it is determined that the calibration parameters obtained after the m-1 original images undergo iterative learning cannot be used to calibrate the images.
  • the electronic device determines whether the m-th original image can be calibrated based on the calibration parameters of the m-1 original image by determining whether the number of m-1 meets the preset condition.
  • subsequent operations may also be triggered by other judgment methods.
  • an algorithm can be used to estimate whether the spatial noise of each original image in the m-1 original image is less than a certain value, and then it can be determined whether the m-th original image can be performed based on the calibration parameters of the m-1 original image. calibration.
  • S441 Calibrate the m-th original image based on the calibration parameters obtained after iterative learning of the m-1th original image.
  • the electronic device determines that the calibration parameters obtained after iterative learning of the m-1 original images can be used to calibrate the original image when acquiring the original image, calibrate the calibration parameters based on the calibration parameters obtained after the m-1 original image undergoes iterative learning The mth original image.
  • iterative learning is performed on the m-th original image. Specifically, when it is determined that there is no abnormality in the m-th original image, iterative learning is performed on the m-th original image.
  • the electronic device determines whether the number of original images (m-1) used for iterative learning is greater than or equal to a third threshold, and determines the second weight value corresponding to the pixel value of the m-th original image based on the determination result, and The first weight value corresponding to the calibration parameter obtained after the m-1 original images undergoes iterative learning, and the sum of the first weight value and the second weight value is 1.
  • S470 Determine a second weight value corresponding to the pixel value of the m-th original image according to the value of m-1.
  • the second weight value corresponding to the pixel value of the m-th original image is determined according to the value of m-1 .
  • the electronic device determines that the number (m-1) of the original images used for iterative learning is greater than or equal to the third preset value, it determines the second corresponding to the pixel value of the m-th original image according to the value of m-1. Weights.
  • the second weight value gradually decreases to a constant value as the number of the original images used for iterative learning increases.
  • the second weight value may be a weight value determined by another preset protocol.
  • the preset protocol may be a preset strategy or a preset rule for determining the second weight value.
  • the electronic device may determine the second weight value according to the following preset protocol:
  • the second weight value may be increased, the m-th original image is an abnormal image or there is abnormal data, and the electronic device uses the m-th original image
  • the second weight value can be appropriately adjusted.
  • the electronic device may also learn from scratch intermittently or periodically.
  • the iterative learning of the original image in the embodiment of the present application may be local learning or global learning, which is not specifically limited in this application.
  • the electronic device can learn locally for the original images that meet the learning conditions collected within a specific time, or perform global learning for all the original images that meet the learning conditions.
  • the light received by the optical fingerprint sensor 230 mainly includes leakage light (indicated by PL) and medium light (indicated by PM).
  • the light leakage may include light directly emitted by the light source toward the optical fingerprint sensor 230 and light reflected by the light source toward the optical fingerprint sensor 230 through obstacles such as the display screen 220.
  • the medium light may include reflected light and transmitted light of pressing a medium (for example, a finger).
  • the media light can be further divided into media light that carries fingerprint information (indicated by PMF) and media light that does not carry fingerprint information (indicated by PMD).
  • the light carrying fingerprint information is mainly concentrated on the surface of the cover glass 221.
  • the signal carrier arrives at different heights from the optical fingerprint sensor 230 from the signal source
  • the propagation path of the optical fingerprint sensor 230 is not consistent, so the calibration information required when calibrating the fingerprint image is also not consistent.
  • the light leakage is closer to the fingerprint optical fingerprint sensor 230 than the medium light, so the calibration information required by the two is not completely consistent.
  • the output electrical signals may also be inconsistent.
  • the relationship between the electrical signal output by the nth Pixel of the optical fingerprint sensor 230 and the received light intensity can be expressed by the following formula:
  • V n represents the output electrical signal of the nth Pixel of the optical fingerprint sensor 230
  • b n represents the response difference of the nth Pixel
  • the double underline represents the variable with a mean value of 1
  • P n represents the light intensity of the nth Pixel received light
  • PL n represents the light intensity of the leaked light received by the nth Pixel
  • PMF n represents the light intensity of the medium light that carries fingerprint information received by the nth Pixel
  • PMD n represents the light intensity of the medium light that does not carry fingerprint information received by the nth Pixel
  • the total number of pixels of the optical fingerprint sensor 230 is N .
  • the fingerprint recognition process may be performed in various changing scenarios (for example, the signal source intensity ⁇ changes, the finger reflectivity ⁇ changes, and/or the signal carrier propagation path ⁇ changes, etc.), it is difficult to fully cover with fixed calibration information All changing scenarios.
  • the embodiment of the present application can update the calibration information of the optical fingerprint sensor 230 through continuous learning of fingerprint images, so as to achieve the purpose of eliminating interference in various changing scenes.
  • the propagation path of the leakage light and the propagation path of the medium light are respectively expressed by the following formulas:
  • the signal source also has uneven intensity distribution, that is, the signal source intensity corresponding to each pixel of the optical fingerprint sensor 230 is not consistent.
  • the signal source intensity corresponding to each pixel of the optical fingerprint sensor 230 is quantified by the following formula:
  • ⁇ n represents the intensity of the signal source corresponding to the nth Pixel of the optical fingerprint sensor 230.
  • ⁇ n can be divided into ⁇ and Double underscores indicate variables with a mean of unit 1. During the fingerprint recognition process, It is basically unchanged or changes slowly, while ⁇ will change significantly or rapidly.
  • the light received by the optical fingerprint sensor 230 can be quantified.
  • the leakage light PL n received by the nth Pixel of the optical fingerprint sensor 230 can be determined by the signal source intensity ⁇ and The propagation path ⁇ L n of the leaked light and the leaked light reflectance ⁇ L are represented.
  • the medium light PMF n that carries fingerprint information received by the nth Pixel of the optical fingerprint sensor 230 can use the signal source intensity ⁇ and The propagation path ⁇ M n of the medium light and the fingerprint signal rate ⁇ FP are expressed.
  • the medium light PMD n that does not carry fingerprint information received by the nth Pixel of the optical fingerprint sensor 230 can use the signal source intensity ⁇ and The propagation path ⁇ M n of the medium light and the finger reflectivity ⁇ M are represented.
  • the optical signal received by the nth Pixel of the optical fingerprint sensor 230 can be expressed by the following formula:
  • V n is equal to b n .
  • all V n is subtracted from b n to obtain:
  • the embodiment of the present application only takes the change of any one of the signal source intensity ⁇ , finger reflectivity ⁇ , and propagation path ⁇ as an example for analysis, but it should not be understood as a specific limitation on itself.
  • the analysis process and formula derivation do not consider the influence of secondary factors such as time domain noise.
  • the electronic device may determine the first calibration parameter according to the following iterative learning formula:
  • Klm(i+1) (1-T(i+1))*Klm(i)+T(i+1)*FP/uFP;
  • the Klm(i+1) represents the calibration parameters obtained after the i+1th original image undergoes iterative learning
  • the T(i+1) represents the i+1th original image undergoes iterative learning
  • the weight value of the calibration parameter obtained later the FP represents the pixel value of the (i+1)th original image
  • the uFP represents the average value of the pixel value of the (i+1)th original image.
  • the electronic device After acquiring the first calibration parameter, the electronic device calibrates the target image according to the following calibration formula and the first calibration parameter to obtain the calibration image:
  • the CaliFP represents the correction value of the pixel value of the (i+1)th original image.
  • the following is an analysis of the iterative learning process and calibration effect of the n original images based on the technical solution of the first embodiment when the electronic device is in different application scenarios.
  • the light signal received by the nth pixel of the mth fingerprint image in the M sheets can be expressed by the following formula:
  • the received optical signal can be further expressed as:
  • the calibration method of the embodiment of the present application can eliminate most of the interference, leaving only a small amount of interference, and basically does not affect fingerprint recognition performance.
  • the calibration effect is basically not affected, indicating that the technical solution of this embodiment can be compatible with scenes where the intensity ⁇ of the signal source changes drastically or rapidly.
  • the received optical signal can be further expressed as:
  • the change of the finger reflectivity ⁇ will affect the calibration result, thereby affecting the fingerprint recognition performance.
  • the change of finger reflectivity ⁇ will affect the calibration effect.
  • the technical solutions of the embodiments of the present application can still calibrate fingerprint images with changing finger reflectivity ⁇ , especially for scenes where the finger reflectivity ⁇ changes slightly or slowly, which can reduce the finger reflectivity ⁇ . The impact of changes on fingerprint images.
  • the optical signal can be further expressed as:
  • the propagation path ⁇ of the signal carrier will affect the calibration result, which in turn affects the fingerprint recognition performance.
  • changes in the propagation path ⁇ will affect the calibration effect.
  • the technical solutions of the embodiments of the present application can still calibrate fingerprint images with changes in the propagation path ⁇ , especially for scenarios where the propagation path ⁇ changes slightly or slowly, which can reduce the effect of changes in the propagation path ⁇ on the fingerprint. The impact of the image.
  • the electronic device may determine the first calibration parameter according to the following iterative learning formula:
  • the Blm(i+1) represents the calibration parameters obtained after the i+1th original image undergoes iterative learning
  • the T(i+1) represents the i+1th original image undergoes iterative learning
  • the weight value of the calibration parameter obtained later, the FP represents the pixel value of the i-th original image.
  • the target image may be calibrated according to the following calibration formula and the first calibration parameter to obtain the calibration image:
  • the CaliFP represents the correction value of the pixel value of the (i+1)th original image.
  • the light signal received by the nth pixel of the mth fingerprint image in the M sheets can be expressed by the following formula:
  • the received optical signal can be further expressed as:
  • the average of the M fingerprint images can be obtained:
  • the change of the intensity ⁇ of the signal source will affect the calibration result, and then affect the fingerprint recognition performance.
  • the change of the intensity ⁇ of the signal source will affect the calibration effect.
  • the technical solutions of the embodiments of the present application can still calibrate the fingerprint image whose intensity ⁇ of the signal source changes, especially for scenarios where the intensity ⁇ of the signal source changes slightly or slowly, which can reduce the signal source's intensity. The influence of the change of intensity ⁇ on the fingerprint image.
  • the received optical signal can be further expressed as:
  • the change of the finger reflectivity ⁇ will affect the calibration result, thereby affecting the fingerprint recognition performance.
  • the change of finger reflectivity ⁇ will affect the calibration effect.
  • the technical solutions of the embodiments of the present application can still calibrate fingerprint images with changing finger reflectivity ⁇ , especially for scenes where the finger reflectivity ⁇ changes slightly or slowly, which can reduce the finger reflectivity ⁇ . The impact of changes on fingerprint images.
  • the optical signal can be further expressed as:
  • the propagation path ⁇ of the signal carrier will affect the calibration result, which in turn affects the fingerprint recognition performance.
  • changes in the propagation path ⁇ will affect the calibration effect.
  • the technical solutions of the embodiments of the present application can still calibrate fingerprint images with changes in the propagation path ⁇ , especially for scenarios where the propagation path ⁇ changes slightly or slowly, which can reduce the effect of changes in the propagation path ⁇ on the fingerprint. The impact of the image.
  • the application also provides a device for calibrating images.
  • FIG. 6 is a schematic block diagram of an apparatus 500 for calibrating an image according to an embodiment of the present application.
  • the device 500 may include a determination unit 510 and a calibration unit 520.
  • the determining unit 510 is configured to determine the first calibration parameter through iterative learning of n original images; wherein the calibration parameter obtained after iterative learning of the i+1th original image among the n original images is based on the n
  • the calibration parameters of the i-th original image in the original images obtained after iterative learning and the parameters determined by the pixel value of the i+1-th original image, the first calibration parameter is the value of the n original images
  • Calibration parameters of the nth original image after iterative learning, n is the first preset value and a positive integer, 1 ⁇ i ⁇ n.
  • the calibration unit 520 is configured to calibrate the target image based on the first calibration parameter.
  • the target image is the n+jth original image, and j is a positive integer; wherein, the calibration unit 520 is specifically configured to:
  • the second calibration parameter is obtained through iterative learning of part or all of the original images from the n+1th to n+j original images; +1 original image for calibration.
  • the original image used for iterative learning is an image whose light intensity is less than a second preset value.
  • the calibration parameter obtained after the iterative learning of the i-th original image corresponds to the first weight value
  • the pixel value of the i+1-th original image corresponds to the second weight value
  • the sum of the first weight value and the second weight value is 1.
  • the second weight value gradually decreases to a constant value as the number of the original images used for iterative learning increases.
  • the determining unit 510 is specifically configured to:
  • Klm(i+1) (1-T(i+1))*Klm(i)+T(i+1)*FP/uFP;
  • the Klm(i+1) represents the calibration parameters obtained after the i+1th original image undergoes iterative learning
  • the T(i+1) represents the i+1th original image undergoes iterative learning
  • the weight value of the calibration parameter obtained later the FP represents the pixel value of the (i+1)th original image
  • the uFP represents the average value of the pixel value of the (i+1)th original image.
  • the calibration unit 520 is specifically configured to:
  • the target image is calibrated according to the following calibration formula and the first calibration parameter to obtain the calibration image:
  • the CaliFP represents the correction value of the pixel value of the (i+1)th original image.
  • the determining unit 510 is specifically configured to:
  • the Blm(i+1) represents the calibration parameters obtained after the i+1th original image undergoes iterative learning
  • the T(i+1) represents the i+1th original image undergoes iterative learning
  • the weight value of the calibration parameter obtained later, the FP represents the pixel value of the i-th original image.
  • the calibration unit 520 is specifically configured to:
  • the target image is calibrated according to the following calibration formula and the first calibration parameter to obtain the calibration image:
  • the CaliFP represents the correction value of the pixel value of the (i+1)th original image.
  • the present application also provides an electronic device, which may include a display screen, a fingerprint module, and the device for calibrating an image mentioned above; the fingerprint module is arranged below the display screen or The inside of the display screen; the fingerprint module is electrically connected to the device for calibrating images; wherein the fingerprint module is used to receive fingerprint detection signals returned by the reflection or scattering of a human finger above the display screen , The fingerprint detection signal carries fingerprint information of the finger.
  • an electronic device may include a display screen, a fingerprint module, and the device for calibrating an image mentioned above; the fingerprint module is arranged below the display screen or The inside of the display screen; the fingerprint module is electrically connected to the device for calibrating images; wherein the fingerprint module is used to receive fingerprint detection signals returned by the reflection or scattering of a human finger above the display screen , The fingerprint detection signal carries fingerprint information of the finger.
  • apparatus 500 may correspond to a corresponding main body that executes each method embodiment in FIG. 4 and FIG. 5 according to the present application. For brevity, details are not described herein again.
  • the apparatus for calibrating an image according to an embodiment of the present application is described above from the perspective of functional modules in conjunction with FIG. 6. It should be understood that the functional module can be implemented in the form of hardware, can also be implemented in the form of software instructions, or can be implemented in a combination of hardware and software modules.
  • the steps of the method embodiments in the embodiments of the present application can be completed by hardware integrated logic circuits in the processor and/or instructions in the form of software, and the steps of the methods disclosed in the embodiments of the present application can be directly embodied as hardware.
  • the execution of the decoding processor is completed, or the execution is completed by a combination of hardware and software modules in the decoding processor.
  • the software module may be located in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, and registers.
  • the storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps in the foregoing method embodiments in combination with its hardware.
  • both the determining unit 510 and the calibration unit 520 shown in FIG. 6 may be implemented by a processor, wherein the calibration parameters determined by the determining unit 510 may be stored in a memory.
  • FIG. 7 is a schematic structural diagram of an apparatus 600 for fingerprint identification according to an embodiment of the present application.
  • the device 600 shown in FIG. 7 includes a fingerprint sensor chip 610, a processor 620, and a memory 630.
  • the fingerprint sensor chip 610 can be used to obtain fingerprint information. For example, when the processor 620 determines that the pressing force of the user pressing the collection area in the display screen is greater than or equal to the trigger threshold, it triggers the fingerprint sensor chip 610 to acquire the fingerprint information, that is, triggers the fingerprint sensor chip 610 to pair Fingerprint data collection operation.
  • the memory 630 may be used to store the aforementioned fingerprint information for fingerprint registration or fingerprint identification, and may also be used to store codes and instructions executed by the processor 620. For example, the calibration parameter determined by the processor 620.
  • the processor 620 may call and run a computer program from the memory 630 to implement the method in the embodiment of the present application.
  • the memory 630 may be a separate device independent of the processor 620, or may be integrated in the processor 620.
  • the device 600 may correspond to the device 500 in the embodiment of the present application, and may correspond to the corresponding main body that executes each method embodiment in FIG. 3 and FIG. 4 according to the present application. For brevity, it will not be omitted here. Repeat.
  • the various components in the device 600 are connected by a bus system, where in addition to a data bus, the bus system also includes a power bus, a control bus, and a status signal bus.
  • processor mentioned in the embodiments of the present application may be an integrated circuit chip with signal processing capability, and can implement or execute the methods, steps, and logical block diagrams disclosed in the embodiments of the present application.
  • the above-mentioned processor may be a general-purpose processor, a digital signal processor (digital signal processor, DSP), an application specific integrated circuit (ASIC), a ready-made programmable gate array (field programmable gate array, FPGA), or Other programmable logic devices, transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.
  • the memory mentioned in the embodiments of the present application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.
  • the non-volatile memory can be read-only memory (ROM), programmable read-only memory (programmable ROM, PROM), erasable programmable read-only memory (erasable PROM, EPROM), and electronic Erase programmable read-only memory (electrically EPROM, EEPROM) or flash memory.
  • ROM read-only memory
  • PROM programmable read-only memory
  • EPROM erasable programmable read-only memory
  • EPROM erasable PROM
  • EPROM erasable programmable read-only memory
  • electronic Erase programmable read-only memory electrically EPROM, EEPROM
  • flash memory electrically EPROM, EEPROM
  • the volatile memory may be random access memory (RAM), which is used as an external cache.
  • the memory in the embodiment of the present application may also be static random access memory (static RAM, SRAM), dynamic random access memory (dynamic RAM, DRAM), Synchronous dynamic random access memory (synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (double data rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (enhanced SDRAM, ESDRAM), synchronous connection Dynamic random access memory (synch link DRAM, SLDRAM) and direct memory bus random access memory (Direct Rambus RAM, DR RAM), etc.
  • FIG. 8 is a schematic structural diagram of an electronic device (such as a touch screen mobile phone) 700 to which an embodiment of the present application is applied. As shown in FIG. 8, the electronic device 700 may include:
  • a processor 710 a processor 710, a memory 720, and a touch screen 730.
  • the touch display screen 730 includes a pressure sensor 731, and the pressure sensor 731 is used to sense the pressure of the touch input signal on the touch display screen 730.
  • the processor 710 is configured to receive a pressure signal sensed by the pressure sensor 731, and to process the pressure signal, for example, to trigger an application in the mobile terminal 100 based on the pressure signal.
  • the electronic device 700 may further include a fingerprint sensor chip 780, and the fingerprint sensor chip 780 is used to obtain a fingerprint image (ie, an original image).
  • the fingerprint sensor chip 780 may include a device for fingerprint identification (for example, the device 500 shown in FIG. 6 or the device 600 shown in FIG. 7), which is used to perform image calibration on the fingerprint image.
  • the electronic device 700 may further include an illuminance sensor 790 for determining whether the touch display screen 730 is blocked.
  • the electronic device may also include other components, such as the audio circuit 740, the power supply 750, the WiFi module 760, and the radio frequency circuit 770 as shown in FIG. 1.
  • the power supply 750 may include a visible light source and an infrared light source, wherein the visible light emitted by the visible light source is used for displaying images, and the infrared light emitted by the infrared light source is used for fingerprint identification.
  • FIG. 8 is only an example of this application, and should not be construed as a limitation to this application.
  • the fingerprint sensor chip 780 may be arranged inside the touch display screen 730, or the fingerprint sensor chip 780 and a device for fingerprint identification (for example, as shown in FIG. 6 The device 500 or the device 600 as shown in FIG. 7) may be physically separated.
  • the device shown in FIG. 7 may also be applied to electronic equipment that does not include a display screen.
  • electronic equipment that does not include a display screen.
  • fingerprint access control machine or punch card machine and so on For example, fingerprint access control machine or punch card machine and so on.
  • the technical solutions of the embodiments of the present application can be embodied in the form of software products in essence or the parts that contribute to the prior art or the parts of the technical solutions, and the computer software products are stored in a storage medium.
  • Including several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory, random access memory, magnetic disk or optical disk and other media that can store program codes.
  • the division of units or modules or components in the device embodiments described above is only a logical function division, and there may be other divisions in actual implementation.
  • multiple units or modules or components can be combined or integrated.
  • To another system, or some units or modules or components can be ignored or not executed.
  • the units/modules/components described as separate/display components may or may not be physically separated, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units/modules/components may be selected according to actual needs to achieve the objectives of the embodiments of the present application.

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Abstract

La présente invention concerne un procédé et un appareil d'étalonnage d'image et un dispositif électronique. Le procédé consiste : à déterminer un premier paramètre d'étalonnage au moyen d'un apprentissage itératif sur n images d'origine, un paramètre d'étalonnage acquis après l'apprentissage itératif d'une (i+)ème image d'origine dans les n images d'origine étant un paramètre déterminé en fonction d'un paramètre d'étalonnage acquis après l'apprentissage itératif d'une ième image d'origine dans les n images d'origine et d'une valeur de pixel de la (i+)ème image d'origine, le premier paramètre d'étalonnage étant un paramètre d'étalonnage acquis après l'apprentissage itératif de la nième image d'origine dans les n images d'origine, n étant une première valeur prédéfinie et étant un nombre entier positif, et 1 ≤ i ≤ n ; et l'étalonnage d'une image cible sur la base du premier paramètre d'étalonnage. Un paramètre d'étalonnage destiné à étalonner une image d'empreinte digitale peut être mis à jour par l'apprentissage constant d'images d'origine dans un processus d'application d'utilisateur sans dépendre d'informations antérieures ou en fonction d'informations antérieures peu nombreuses de sorte qu'une procédure d'opération peut être simplifiée et la précision d'étalonnage peut en outre être efficacement améliorée.
PCT/CN2019/082739 2019-04-15 2019-04-15 Procédé et appareil d'étalonnage d'image et dispositif électronique WO2020210954A1 (fr)

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