WO2022166316A1 - Procédé et appareil d'ajout de lumière pour la reconnaissance faciale, dispositif de reconnaissance faciale et système associé - Google Patents

Procédé et appareil d'ajout de lumière pour la reconnaissance faciale, dispositif de reconnaissance faciale et système associé Download PDF

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WO2022166316A1
WO2022166316A1 PCT/CN2021/132166 CN2021132166W WO2022166316A1 WO 2022166316 A1 WO2022166316 A1 WO 2022166316A1 CN 2021132166 W CN2021132166 W CN 2021132166W WO 2022166316 A1 WO2022166316 A1 WO 2022166316A1
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face
image
light
preset
historical
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Chinese (zh)
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陈文龙
周杰
卢道和
方镇举
翁玉萍
黄涛
袁文静
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WeBank Co Ltd
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WeBank Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/161—Detection; Localisation; Normalisation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/172—Classification, e.g. identification

Definitions

  • the present application relates to the technical field of face recognition, and in particular, to a method and device for face recognition supplementary light, a face recognition device and a system thereof.
  • Face recognition technology is an important method for user authentication, and its recognition accuracy will directly affect the security of user accounts.
  • insufficient ambient light will reduce the success rate of face recognition.
  • most of the face recognition access control systems that use face recognition technology do not integrate the camera fill light strategy. Even if the fill light strategy is integrated, the light intensity when the face image is collected is usually obtained through the light sensor. Once the light intensity reaches When a certain preset threshold is set, the switch will be controlled to start the fill light program to fill the light intensity with a fixed intensity, or linearly adjust the fill light intensity to a fixed value according to the current light intensity. In addition, you can directly adjust the backlight compensation parameters for the face image collected by the camera to achieve the purpose of filling light.
  • the above-mentioned supplementary light strategy has many problems.
  • the light intensity obtained by the set light sensor not only has certain errors, but also increases the time-consuming of the face recognition process, and frequent calls will also cause great consumption of hardware resources.
  • the light intensity of the face will directly affect the quality of the face image obtained by the access control device. If the light intensity is too bright or too dark, the quality of the collected face image will be low, which will affect the pass rate of face recognition.
  • due to uncontrollable factors such as face placement angle and distance during face collection, there will be different requirements for different placement angles or distance light intensity.
  • the supplementary light strategy that only increases the fixed supplementary light or linearly adjusts the supplementary light intensity to a fixed value based on the light intensity collected by the light sensor obviously cannot meet the requirements of the actual working conditions, and the supplementary light accuracy is low. Furthermore, in the prior art, the supplementary light strategy that directly adjusts the backlight compensation to the face image collected by the camera does not fundamentally solve the problem that supplementary light needs to be performed due to insufficient light. This supplementary light strategy improves face recognition. The pass rate is very limited.
  • the present application provides a face recognition fill light method, device, face recognition device and system thereof, which are used to overcome the existing fill light strategy that relies on hardware to obtain fill light intensity to perform fill light error, which is time-consuming and takes a long time.
  • the hardware resource consumption is large and cannot meet the needs of actual working conditions, and the purpose of filling light through backlight compensation is not conducive to improving the pass rate of face recognition.
  • the present application provides a face recognition supplementary light method, including:
  • the to-be-processed image parameters are determined according to the to-be-recognized face image and a preset image processing model, where the to-be-processed image parameters include the current brightness, first distance, and first angle of the to-be-recognized face image, and the first The distance represents the distance between the face to be recognized and the face recognition device, and the first angle represents the angle at which the face to be recognized deviates from the face recognition device;
  • the current supplementary light intensity is determined according to the current brightness of the face image to be recognized, the first distance and the first angle, so as to control the supplementary light to perform supplementation according to the current supplementary light intensity Light.
  • the method before the current fill light intensity is determined based on the preset fill light model according to the current brightness of the face image to be recognized, the first distance and the first angle, the method further includes: :
  • a respective multiple linear regression model is generated according to each historical face image and the corresponding historical recognition result
  • the optimal regression parameter combination is determined according to all multiple linear regression models, and the preset supplementary light model is determined according to the preset multiple linear regression model and the optimal regression parameter combination.
  • a respective multiple linear regression model is generated according to each historical face image and the corresponding historical recognition result, including:
  • each historical image parameter includes the current brightness of the historical face image, a second distance and a second angle
  • the second distance represents the distance between the historical face and the face recognition device
  • the first The two angles represent the angle at which the historical face deviates from the face recognition device.
  • determining the preset supplementary light model according to the preset multiple linear regression model and the optimal regression parameter combination includes:
  • the preset multiple linear regression model after configuring the corresponding regression parameters is determined as the preset supplementary light model.
  • determining the current fill light intensity based on the preset fill light model according to the current brightness of the face image to be recognized, the first distance and the first angle including:
  • a target mapping relationship is generated according to the target brightness of the face image to be recognized, the first distance, the first angle and the preset fill light model, and the target mapping relationship is used to represent the target face recognition result and the the functional relationship between the target brightness;
  • the difference value between the corresponding value of the target brightness and the corresponding value of the current brightness of the face image to be recognized is acquired, and the difference value is determined as the current supplementary light intensity.
  • determining the image parameters to be processed according to the face image to be recognized and a preset image processing model includes:
  • the current brightness of the face image to be recognized is determined according to a preset grayscale algorithm in the preset image processing model, and the preset grayscale algorithm is used to represent the corresponding pixels of the face image to be recognized.
  • the distribution function of each gray value is determined according to a preset grayscale algorithm in the preset image processing model, and the preset grayscale algorithm is used to represent the corresponding pixels of the face image to be recognized.
  • the first angle is determined according to a preset image algorithm in the preset image processing model.
  • the method before the determining the first distance according to the preset distance algorithm in the preset image processing model, the method further includes:
  • For each first face sample image determine the ratio of the face area of the first face sample image to the recognition frame area, and obtain the face and the face corresponding to the first face sample image. distance data between the face recognition devices;
  • a distance mapping relationship is generated according to each ratio and the corresponding distance data, and the distance mapping relationship is used to characterize the preset distance algorithm.
  • control after the controlling the supplementary light to perform supplementary light according to the current supplementary light intensity, the control further includes:
  • a training sample is determined from the face sample according to the recognition result, and the training sample is the face sample for which the recognition result obtained after supplementary light is less than a preset threshold;
  • the second face sample image obtained before each training sample is filled with light is used as the historical face image to optimize the optimal regression parameter combination.
  • controlling the fill light to fill light according to the current fill light intensity including:
  • the supplementary light is controlled to weaken the current supplementary light intensity for the current ambient light.
  • the present application provides a face recognition supplementary light device, comprising:
  • a first acquisition module used for acquiring a face image to be recognized
  • the first processing module is used to determine the image parameters to be processed according to the face image to be recognized and a preset image processing model, and the image parameters to be processed include the current brightness, the first distance and the first distance of the face image to be recognized. an angle, the first distance represents the distance between the face to be recognized and the face recognition device, and the first angle represents the angle at which the face to be recognized deviates from the face recognition device;
  • the second processing module is configured to, based on a preset supplementary light model, determine the current supplementary light intensity according to the current brightness of the face image to be recognized, the first distance and the first angle, so as to control the supplementary light according to the selected Fill light according to the current fill light intensity.
  • the face recognition light supplement device further includes:
  • the second acquisition module is used to acquire a plurality of historical face images and the respective historical recognition results corresponding to each historical face image
  • the third processing module is used to generate the respective multiple linear regression model according to each historical face image and the corresponding historical recognition result based on the preset multiple linear regression model;
  • the fourth processing module is configured to determine an optimal regression parameter combination according to all multiple linear regression models, and determine the preset supplementary light model according to the preset multiple linear regression model and the optimal regression parameter combination.
  • the third processing module is specifically used for:
  • each historical image parameter includes the current brightness of the historical face image, a second distance and a second angle
  • the second distance represents the distance between the historical face and the face recognition device
  • the first The two angles represent the angle at which the historical face deviates from the face recognition device.
  • the fourth processing module is specifically used for:
  • the preset multiple linear regression model after configuring the corresponding regression parameters is determined as the preset supplementary light model.
  • the second processing module is further used for:
  • a target mapping relationship is generated according to the target brightness of the face image to be recognized, the first distance, the first angle and the preset fill light model, and the target mapping relationship is used to represent the target face recognition result and the the functional relationship between the target brightness;
  • the difference value between the corresponding value of the target brightness and the corresponding value of the current brightness of the face image to be recognized is acquired, and the difference value is determined as the current supplementary light intensity.
  • the first processing module is specifically used for:
  • the current brightness of the face image to be recognized is determined according to a preset grayscale algorithm in the preset image processing model, and the preset grayscale algorithm is used to represent the corresponding pixels of the face image to be recognized.
  • the distribution function of each gray value is determined according to a preset grayscale algorithm in the preset image processing model, and the preset grayscale algorithm is used to represent the corresponding pixels of the face image to be recognized.
  • the first angle is determined according to a preset image algorithm in the preset image processing model.
  • the first processing module is further used for:
  • For each first face sample image determine the ratio of the face area of the first face sample image to the recognition frame area, and obtain the face and the face corresponding to the first face sample image. distance data between the face recognition devices;
  • a distance mapping relationship is generated according to each ratio and the corresponding distance data, and the distance mapping relationship is used to characterize the preset distance algorithm.
  • the face recognition light supplement device further includes: a fifth processing module; the fifth processing module is used for:
  • a training sample is determined from the face sample according to the recognition result, and the training sample is the face sample for which the recognition result obtained after supplementary light is less than a preset threshold;
  • the second face sample image obtained before each training sample is filled with light is used as the historical face image to optimize the optimal regression parameter combination.
  • the second processing module is further used for:
  • the supplementary light is controlled to weaken the current supplementary light intensity for the current ambient light.
  • the present application provides a face recognition device, including:
  • a memory for storing a computer program for the processor
  • the processor is configured to execute any one of the possible face recognition and light supplementation methods provided in the first aspect by executing the computer program.
  • the present application provides a computer-readable storage medium on which a computer program is stored, and the computer program is used to execute any one of the possible face recognition and light-filling methods provided in the first aspect.
  • the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements any one of the possible face recognition and light supplementation methods provided in the first aspect.
  • the present application provides a face recognition system, including: any possible face recognition device provided in the third aspect and an access control device communicatively connected to the face recognition device;
  • the access control device is used for selecting whether to open the access control according to the corresponding recognition result of the face recognition device.
  • the present application provides a method, a device, a face recognition device and a system for supplementing light for face recognition.
  • the face recognition fill light method first obtains the face image to be recognized, then determines the parameters of the image to be processed according to the face image to be recognized and a preset image processing model, and finally, based on the preset fill light model, according to the determined image parameters to be processed.
  • the current brightness, the first distance, and the first angle of the face image to be recognized in the device determine the current fill light intensity, and then control the fill light to fill the camera of the face recognition device according to the current fill light intensity, so as to complete the face recognition. fill light operation in .
  • the first distance represents the distance between the face to be recognized and the face recognition device
  • the first angle represents the angle at which the face to be recognized deviates from the face recognition device.
  • the face recognition fill light method provided by this application is based on a preset fill light model, determines the current fill light intensity according to the image parameters to be processed, does not need to rely on the hardware resources of the light sensor, and can obtain the current fill light in real time and accurately according to the face to be recognized.
  • the supplementary light intensity meets the needs of different light requirements in actual working conditions. And fundamentally solve the problem of fill light to achieve the purpose of fill light, which is beneficial to improve the pass rate of face recognition.
  • FIG. 1 is a schematic diagram of an application scenario provided by an embodiment of the present application.
  • FIG. 2 is a schematic flowchart of a method for supplementing light for face recognition provided by an embodiment of the present application
  • FIG. 3 is a schematic flowchart of another face recognition fill light method provided by an embodiment of the present application.
  • FIG. 4 is a schematic flowchart of yet another method for face recognition supplementary light provided by an embodiment of the present application.
  • FIG. 5 is a schematic flowchart of another method for replenishing light for face recognition provided by an embodiment of the present application.
  • FIG. 6 is a schematic flowchart of another method for face recognition supplementary light provided by an embodiment of the present application.
  • FIG. 7 is a schematic structural diagram of a face recognition light supplement device provided by an embodiment of the present application.
  • FIG. 8 is a schematic structural diagram of another face recognition supplementary light device provided by an embodiment of the present application.
  • FIG. 9 is a schematic structural diagram of a face recognition device according to an embodiment of the present application.
  • the face recognition device integrated with the supplementary light strategy in the prior art usually needs to obtain the light intensity when the face image is collected through the light sensor.
  • the switch is controlled to start the supplementary light program to supplement the light intensity with a fixed intensity, or linearly adjust the supplementary light intensity to a fixed value according to the current light intensity.
  • the quality of the collected face image will be low when the light is too bright or too dark, and there are uncontrollable factors such as placement angle and distance when the face image is collected. For different placement angles or distances, the light intensity will be lower. have different requirements. Therefore, the supplementary light strategy that only increases the fixed supplementary light or linearly adjusts the supplementary light intensity to a fixed value based on the light intensity collected by the light sensor obviously cannot meet the requirements of actual working conditions, and the supplementary light accuracy is low. In addition, the supplementary light strategy that directly adjusts the backlight compensation to the face image collected by the camera does not fundamentally solve the problem of supplementary light due to insufficient light. The face recognition pass rate improved by this supplementary light strategy is very limited. It can be seen that, in view of the above-mentioned technical defects in the prior art, a supplementary light strategy for face recognition is urgently needed.
  • the present application provides a method, a device, a face recognition device and a system for supplementing light for face recognition.
  • the inventive concept of the face recognition fill light method provided by the present application is as follows: first, the parameters of the to-be-processed image are determined according to the collected face-to-be-recognized image and the preset image processing module, that is, to determine the current brightness, The distance between the face to be recognized and the face recognition device, and the angle at which the face to be recognized deviates from the face recognition device.
  • the corresponding characterization parameters are determined by the face image to be recognized, without relying on the light sensor to obtain the light intensity, and there is no problem of fill-light error, time-consuming and hardware resource consumption.
  • the current fill light intensity is determined according to the obtained characteristic parameters, and then the fill light is controlled to fill the camera of the face recognition device according to the determined current fill light intensity. It can be seen that the current fill light intensity is determined based on the preset fill light model and the to-be-processed image parameters of the face image to be recognized, rather than the face recognition device setting a fixed fill light intensity or fill light to a fixed value. Therefore, the supplementary light strategy provided in this application is real-time and accurate. Combining the above two points, the supplementary light strategy provided by the present application fundamentally solves the supplementary light problem due to insufficient light, which is beneficial to improve the pass rate of the face recognition device.
  • FIG. 1 is a schematic diagram of an application scenario provided by an embodiment of the present application.
  • the face recognition supplementary light method provided by the embodiment of the present application may be performed by the face recognition supplementary light device provided by the embodiment of the present application, and specifically, the face recognition supplementary light device 11 provided by the embodiment of the present application may be
  • the computer program can be configured in the processor of the face recognition device 12, so that the face recognition device 12 is integrated with the method provided by the embodiment of the present application.
  • the face recognition fill light method is to implement a fill light strategy when performing face recognition to achieve a fill light effect and improve the pass rate of the face recognition device 12 .
  • the face recognition device 12 can be applied to the access control security system, can also be applied to various face recognition occasions such as financial security and other identity verification, or can be applied to the video conference human-computer interaction system, the attendance system and other occasions, In this regard, this embodiment does not limit it.
  • the specific specifications and types of the face recognition device 12 are not shown in FIG. 1 , and a suitable device can be selected and set according to the actual application in actual working conditions.
  • FIG. 2 is a schematic flowchart of a method for supplementing light for face recognition provided by an embodiment of the present application. As shown in FIG. 2 , the face recognition fill light method provided in this embodiment includes:
  • S101 Acquire a face image to be recognized.
  • the image of the face to be recognized is collected through the camera of the face recognition device, that is, the image of the face to be recognized is acquired. For example, if the face to be recognized is within the image collection range of the camera, the image of the face to be recognized can be acquired through the camera.
  • the specification of the camera, the setting position of the camera on the face recognition device, and the image acquisition range formed may be set according to the specific application of the face recognition device, which is not limited in this embodiment.
  • S102 Determine parameters of the image to be processed according to the face image to be recognized and a preset image processing model.
  • the parameters of the image to be processed include the current brightness of the face image to be recognized, the first distance, and the first angle, the first distance represents the distance between the face to be recognized and the face recognition device, and the first angle represents the face to be recognized. An angle off the face recognition device.
  • a preset image processing model is used to determine the parameters of the image to be processed according to the face image to be recognized.
  • the determined to-be-processed image parameters include the current brightness of the to-be-recognized face image, the distance between the to-be-recognized face and the face recognition device, and the angle at which the to-be-recognized face deviates from the face recognition device.
  • the current brightness of the face image to be recognized can be fed back to the current ambient light when the face recognition device acquires the face image to be recognized.
  • the distance between the face to be recognized and the face recognition device is defined as the first distance.
  • the first distance may be the distance between the face to be recognized and the screen of the face recognition device when the face recognition device collects the image of the face to be recognized.
  • the first angle may be the angle at which the face to be recognized deviates from the front face of the face recognition device when the face recognition device collects the face to be recognized.
  • the image is processed to obtain the first angle, and the preset image algorithm such as Rotated Rect.rrect of openCV.
  • the above image parameters to be processed can be obtained by performing corresponding processing or calculation on the face image to be recognized through a preset image processing model.
  • the current brightness of the to-be-recognized face image in the to-be-processed image parameters can be obtained according to the to-be-recognized face image and the preset grayscale algorithm in the preset image processing model.
  • the preset grayscale algorithm refers to a distribution function that can characterize the grayscale values corresponding to all the pixels of the face image to be recognized.
  • the size of its pixel value can be described by grayscale.
  • the grayscale value is in the range of [0, 255], also called grayscale.
  • a preset grayscale algorithm such as a grayscale histogram, is a distribution function about grayscale levels, and is a corresponding function for performing statistics on the distribution of grayscale levels in an image.
  • the grayscale histogram can count all the pixels in the image according to the size of their respective grayscale values to count the frequency of occurrence of each grayscale value, and then obtain the average value of the pixels in the image, and determine the average value as the image. to obtain the current ambient light when the image is obtained through the current brightness feedback.
  • the preset grayscale algorithm can be implemented by the Scalar.mean function of Opencv.
  • use the Scalar.mean function of Opencv to process the face image to be recognized, and the obtained processing result is the current brightness of the face image to be recognized.
  • the preset grayscale algorithm includes but is not limited to the Scalar.mean function of Opencv listed in this embodiment.
  • FIG. 3 is a schematic flowchart of another method for replenishing light for face recognition provided by an embodiment of the present application. As shown in FIG. 3 , in the face recognition fill light method provided by this embodiment, the first distance is determined according to a preset distance algorithm in a preset image processing model, including:
  • S201 Acquire a plurality of first face sample images through a face recognition device to obtain a face area and a recognition frame area corresponding to each first face sample image.
  • the area of the recognition frame refers to the area of the preset frame when the face recognition device performs face recognition.
  • first face sample images through the face recognition device.
  • multiple first face sample images that have been obtained by the face recognition device can be obtained from the video library of the face recognition device.
  • these first face sample images may be different images, and of course, may also include the same image, which is not limited in this embodiment.
  • After obtaining a plurality of first face sample images calculate the face area on each first face sample image and the recognition frame area of the face recognition device that obtained the first face sample image one by one, so as to obtain each first face sample image.
  • the face area refers to the area of the face in the first face sample image.
  • the area of the recognition frame is the area of the preset frame set when the face recognition device performs face recognition. Each face recognition device has a corresponding preset frame area when it collects face images for face recognition.
  • the area is the area of the recognition frame.
  • S202 For each first face sample image, determine the ratio of the face area of the first face sample image to the area of the recognition frame, and obtain the relationship between the face corresponding to the first face sample image and the face recognition device distance data.
  • each first face sample image, and the face area and recognition frame area corresponding to each first face sample image determine the first face sample image for each first face sample image The ratio between the face area and the recognition frame area.
  • the distance data between the face corresponding to the first face sample image and the face recognition device is also obtained, that is, the first face sample image of the first face image is also obtained.
  • the distance mapping relationship can be obtained by sorting the obtained data.
  • S203 Generate a distance mapping relationship according to each ratio and corresponding distance data.
  • the distance mapping relationship is used to represent the preset distance algorithm.
  • S k represents the face area of the first face sample image
  • a k represents the recognition frame area of the first face sample image
  • k represents any one of the multiple first face sample images.
  • S k /A k is the ratio of the face area of the first face sample image to the area of the recognition frame.
  • L k represents the distance data between the face corresponding to the first face sample image and the face recognition device.
  • the relationship represented by the above expression (1) is determined as a distance mapping relationship, that is, according to the ratio of the face area of each first face sample image to the area of the recognition frame and the corresponding distance data of the first face sample image.
  • the distance mapping relationship is defined, and the distance mapping relationship is defined as a preset distance algorithm, and then when the face area and the recognition frame area of the face image to be recognized are known, the face image to be recognized can be determined by the above expression (1). the first distance.
  • S204 Determine the first distance according to the preset distance algorithm, the face area of the face image to be recognized, and the area of the recognition frame corresponding to the face image to be recognized.
  • the face area of the face image to be recognized and the preset frame area of the face recognition device that obtains the face image to be recognized can be obtained, that is, the face to be recognized can be obtained.
  • the face area of the image and the area of the recognition frame corresponding to the face image to be recognized, then, the corresponding distance data can be obtained by using the above expression (1), and the distance data is the face image to be recognized corresponding to the person to be recognized.
  • the distance between the face and the face recognition device that is, the first distance.
  • the first distance is determined according to a preset distance algorithm in a preset image processing model.
  • a plurality of first face sample images are obtained through a face recognition device, and the face area and the recognition frame area corresponding to each first face sample image are obtained, and then for each first face sample image, the first face sample image is determined.
  • the ratio of the face area of the face sample image to the area of the recognition frame, and the distance data between the face corresponding to the first face sample image and the face recognition device is obtained, and finally each obtained first person
  • the ratio of the face sample images and the distance data are used for data sorting, and a distance mapping relationship can be generated, so as to use the generated distance mapping relationship to represent the preset distance algorithm.
  • the preset distance algorithm can be used to determine the distance between the face to be recognized corresponding to the face image to be recognized and the face recognition device, That is, the first distance in the image parameters to be processed.
  • This embodiment provides an effective implementation solution for determining the first distance, which fills the technical gap of obtaining the first distance in the prior art.
  • S103 Based on the preset supplementary light model, determine the current supplementary light intensity according to the current brightness, the first distance and the first angle of the face image to be recognized, so as to control the supplementary light to perform supplementary light according to the current supplementary light intensity.
  • the current fill light intensity is determined based on the preset fill light model, and then the fill light is controlled to accurately fill the light according to the corresponding value of the current fill light intensity. Light, so that the face image to be recognized obtained by the face recognition device after the fill light can overcome the problem of low pass rate of face recognition caused by insufficient ambient light.
  • the fill light lamp is controlled to enhance the current fill light intensity for the current ambient light. If the value corresponding to the current fill light intensity is less than zero, the fill light is controlled to weaken the current fill light intensity for the current ambient light. Correspondingly, if the determined value corresponding to the current fill light intensity is equal to zero, the fill light lamp is controlled not to perform any fill light operation on the current ambient light.
  • the face recognition device re-acquires the face image to be recognized of the face to be recognized based on the ambient light after the fill light, and further according to the face recognition device.
  • the integrated preset recognition algorithm determines the recognition result to complete the relevant recognition operation of the face recognition device to be recognized. This embodiment does not limit the recognition work of the face to be recognized by the face recognition device.
  • the face image to be recognized is first obtained, and then the image to be recognized is processed by using a preset image processing model to obtain the image parameters to be processed, and then based on the preset fill light model and The to-be-processed image parameters determine the current fill light intensity, and then control the fill light to fill light according to the current fill light intensity.
  • the face recognition supplementary light method provided in the embodiment of the present application performs supplementary light, it is not to set a fixed supplementary light intensity in the face recognition device or to supplement the light to a fixed value to achieve the purpose of supplementing light, but to achieve the purpose of supplementing light according to the to-be-recognized light.
  • the to-be-processed image parameters of the face image determine the real-time current fill light intensity, so the fill light implemented is adaptive fill light based on the current ambient light when the face recognition device obtains the face image to be recognized, so that the fill light strategy It can meet the actual requirements of different fill light intensity due to uncontrollable factors such as face placement angle and distance in actual working conditions, and effectively improve the fill light accuracy.
  • the face recognition supplementary light method provided by the embodiment of the present application does not need to rely on the hardware resources of the light sensor, and there are no problems of supplementary light error, long time consumption, and loss of hardware resources. It fundamentally solves the problem of supplementary light due to insufficient ambient light, which is beneficial to improve the pass rate of face recognition.
  • the method for supplementing light for face recognition provided by this embodiment of the present application further includes the steps shown in FIG. 4, which is another face recognition method provided by this embodiment of the present application.
  • FIG. 4 is another face recognition method provided by this embodiment of the present application.
  • Schematic flow chart of the fill light method As shown in Figure 4, this embodiment includes:
  • S301 Acquire a plurality of historical face images and respective historical recognition results corresponding to each historical face image.
  • obtain the face-swiping record of the face recognition device obtain a plurality of historical face images and the respective recognition results corresponding to each historical face image, and define the recognition result corresponding to the historical face image as the historical recognition result, that is, it is possible to obtain To a plurality of historical face images and the respective historical recognition results corresponding to each historical face image.
  • face recognition equipment For face recognition technology, face recognition equipment usually compares the acquired face image with the face image stored in the video library, obtains the comparison result, and then compares the comparison result with the preset The passing thresholds are compared to determine whether the face corresponding to the face image can pass the access control or security recognition system to which the face recognition device belongs. If the comparison result satisfies the preset passing threshold, it indicates that the face can pass the identification, otherwise, it cannot pass the identification.
  • face recognition equipment will express the recognition results in the form of comparison scores.
  • the historical recognition result corresponding to each historical face image is also represented as a comparison score.
  • each historical face image and the historical recognition result corresponding to each historical face image After obtaining each historical face image and the historical recognition result corresponding to each historical face image, based on the preset multiple linear regression model, construct each historical face image and its corresponding historical recognition result. Linear regression model.
  • each historical recognition result By acquiring multiple historical face images and their corresponding historical recognition results, it can be found that there is a certain linear correlation between each historical recognition result and the historical image parameters of the corresponding historical face image. Linear regression models characterize this linear correlation.
  • the historical image parameters of each historical face image may be determined according to the historical face image and a preset image processing model.
  • the historical image parameters of the historical face image are similar to the to-be-processed image parameters of the to-be-recognized face image in the above embodiment.
  • the historical image parameters of the historical face image include the current brightness, the second distance, and the second angle of the historical face image.
  • the distance between the historical face corresponding to the historical face image and the face recognition device is defined as the second distance, and the historical face corresponding to the historical face image is deviated from the face recognition device that obtained the historical face image.
  • the angle is defined as the second angle.
  • a method similar to obtaining the image parameters to be processed according to the face image to be recognized and the preset image processing model can determine the historical image parameters of the historical face image according to the historical face image and the preset image processing model, and the specific determination process can be as follows: Refer to the foregoing detailed steps, which will not be repeated here.
  • the historical image parameters of the historical face image are determined by using a preset image processing model for each historical face image, that is, according to Each historical face image and a preset image processing model determine historical image parameters of each historical face image.
  • the preset multiple linear regression model can be expressed as the following expression (2):
  • x 1 , x 2 and x 3 respectively represent multiple independent variables of the preset multiple linear regression model
  • y represents a dependent variable of the preset multiple linear regression model
  • ⁇ 1 , ⁇ 2 , ⁇ 3 , ⁇ 4 , ⁇ 5 and ⁇ 6 are the regression coefficients in the preset multiple linear regression model
  • ⁇ 0 is the random error term of the preset multiple linear regression model.
  • the current brightness, the second distance and the second angle of the historical face image can be respectively determined as independent variables in the above-mentioned change relationship, and the historical identification result corresponding to the group of independent variables can be determined as the corresponding variable in the above-mentioned change relationship. variable.
  • a multiple linear regression model corresponding to the historical face image can be generated according to the above-mentioned preset multiple linear regression model, the historical image parameters of the historical face image, and the corresponding historical recognition results, so that the Based on the preset multiple linear regression model, a respective multiple linear regression model is generated according to each historical face image and its corresponding historical recognition result.
  • the multiple linear regression model corresponding to all historical face images can be represented by the matrix model shown below.
  • the matrix model is represented as follows:
  • n represents the number of historical face images in the selected face brushing record, which is greater than zero.
  • i represents the number of regression coefficients other than random error terms in the preset multiple linear regression model, and the value of i is greater than zero.
  • S303 Determine an optimal regression parameter combination according to all multiple linear regression models, and determine a preset supplementary light model according to the preset multiple linear regression model and the optimal regression parameter combination.
  • the optimal value of the regression coefficient is determined according to all the multiple linear regression models.
  • the optimal regression parameter combination is determined.
  • the optimal regression parameter combination of ⁇ 0 , ⁇ 1 , ⁇ 2 , ⁇ 3 , ⁇ 4 , ⁇ 5 , and ⁇ 6 is determined according to all the multiple linear regression models.
  • the least squares estimation method can be used to calculate the loss function of each multiple linear regression model, so as to determine each regression parameter in the multiple linear regression model with the smallest loss function as the optimal regression parameter combination.
  • a large number of historical face images can be used to generate the corresponding multiple linear combination regression model, so as to determine the optimal combination of regression parameters based on a large number of sample data.
  • the preset supplementary light model is determined according to the optimal regression parameter combination and the preset multiple linear regression model.
  • the preset supplementary light model is a regression model obtained by substituting each regression parameter in the optimal regression parameter combination for each regression parameter in the preset multiple linear regression model.
  • each optimal regression parameter in the optimal regression parameter combination is, for example, ⁇ ′ 0 , ⁇ ′ 1 , ⁇ ′ 2 , ⁇ ′ 3 , ⁇ ′ 4 , ⁇ ′ 5 , ⁇ ′ 6 , respectively , and then configure each optimized regression parameter as the corresponding regression parameter of the preset multiple linear regression model, and then determine the preset multiple linear regression model with the corresponding regression parameters configured as the preset fill light model.
  • configure each optimized regression parameter as each corresponding regression parameter in the above expression (2) then configure the preset multiple linear regression model of the corresponding regression parameter, that is, the preset supplementary light model, as shown in the following expression (3 )express:
  • Y represents the recognition result represented by the preset supplementary light model
  • X 1 , X 2 , and X3 represent each data in the to-be-processed image parameters of the face image whose recognition result needs to be determined by the preset supplementary light model.
  • the face recognition fill light method Before determining the current fill light intensity according to the current brightness, the first distance and the first angle of the face image to be recognized based on the preset fill light model, the face recognition fill light method provided in the embodiment of the present application further includes determining a preset fill light Model. First, obtain multiple historical face images and the corresponding historical recognition results of each historical face image, and then based on the preset multiple linear regression model, generate respective multiple linear regression models according to each historical face image and the corresponding historical recognition results. , and then determine the optimal regression parameter combination according to all the multiple linear regression models, and finally determine the preset fill light model according to the optimal regression parameter combination and the preset multiple linear regression model, so as to use the preset fill light model for any face image to be recognized according to the preset fill light model. Determine the corresponding current fill light intensity, and then overcome the problem of low pass rate of face recognition due to insufficient ambient light, which can fundamentally solve the fill light problem.
  • the preset supplementary light model can represent the mapping relationship between the image parameters to be processed and the recognition results corresponding to the image parameters to be processed, wherein the recognition results corresponding to the image parameters to be processed are obtained by the face recognition device.
  • the received face image to be recognized is recognized.
  • the purpose of implementing fill light is to overcome the problem of low pass rate of face recognition caused by insufficient ambient light. Therefore, the best fill light effect that can be achieved based on the to-be-processed image parameters of the face image to be recognized can be represented by the maximum value of the recognition result represented by the preset fill-light model.
  • step S103 based on the preset supplementary light model, a possible implementation manner of determining the current supplementary light intensity according to the current brightness, the first distance and the first angle of the face image to be recognized is shown in FIG. 5 .
  • FIG. 5 is a schematic flowchart of another method for supplementing light for face recognition provided by an embodiment of the present application.
  • the current fill light intensity is determined according to the current brightness, the first distance and the first angle of the face image to be recognized, including :
  • S401 Generate a target mapping relationship according to target brightness, a first distance, a first angle, and a preset fill-light model of the face image to be recognized.
  • the target mapping relationship is used to represent the functional relationship between the target face recognition result and the target brightness.
  • the target brightness is the brightness that can be achieved by the face recognition device to obtain the face image to be recognized after fill light
  • the target brightness is an unknown quantity
  • the first distance and the first angle of the face image to be recognized are known quantity.
  • Y ⁇ represents the target face recognition result
  • X ⁇ represents the target brightness
  • X 2 and X 3 represent the first distance and the first angle, respectively.
  • S402 Obtain the maximum value of the target face recognition result according to the target mapping relationship, so as to obtain the corresponding value of the target brightness when the target face recognition result takes the maximum value.
  • the maximum value of the target face recognition result is obtained, and the corresponding value of the target brightness can be obtained when the target face recognition result takes the maximum value.
  • the value of X ⁇ can make the target face recognition result take the maximum value, so as to obtain the corresponding value of the target brightness. brightness.
  • S403 Obtain the difference between the corresponding value of the target brightness and the corresponding value of the current brightness of the face image to be recognized, and determine the difference as the current fill light intensity.
  • the difference value which is the intensity of the light to be supplemented, that is, the difference value is determined. It is the current fill light intensity to further control the fill light lamp to take fill light measures according to the currently determined current fill light intensity.
  • the face recognition fill light method when the current fill light intensity is determined based on the preset fill light model and the to-be-processed parameters of the face image to be recognized, firstly according to the target brightness of the face image to be recognized, the first A distance, a first angle, and a preset fill light model generate a target mapping relationship, and the target mapping relationship is used to represent the functional relationship between the target face recognition result and the target brightness. Then, the maximum value of the target face recognition result is obtained according to the target mapping relationship, and the corresponding value of the target brightness is obtained when the target face recognition result takes the maximum value, and then the corresponding value of the target brightness is compared with the current brightness of the face image to be recognized.
  • the current brightness, first distance and first angle of the face image to be recognized determine the current fill light intensity that needs to be filled, so that the face recognition device after the fill light can be used for the face recognition device to be identified.
  • the ambient light where the face is currently located can obtain the maximum recognition result, thereby solving the problem of low pass rate of face recognition equipment due to insufficient ambient light from the root cause.
  • the method further includes: Set up the verification and optimization process of the fill light model.
  • FIG. 6 is a schematic flowchart of another method for supplementing light by face recognition provided by an embodiment of the present application. As shown in Figure 6, this embodiment includes:
  • the recognition results corresponding to multiple face samples before and after fill light are collected. For example, for a face sample, obtain the recognition result of the face recognition device for the face sample before the fill light, and then re-obtain the recognition result through the face recognition device after filling the current ambient light where the face sample is located.
  • the recognition results corresponding to each face sample before and after fill light can be collected.
  • this embodiment does not limit the number of face samples.
  • the setting of different ambient light intensities is to enrich the data samples to enhance the verification and optimization effect provided by this embodiment.
  • a fill light switch can be configured on the face recognition device, and when the fill light switch is turned on, the face recognition device can perform fill light according to the determined current fill light intensity to obtain a recognition result after fill light. If the fill light switch is turned off, the corresponding identification result before fill light can be obtained.
  • S502 Determine a training sample from the face sample according to the recognition result.
  • the training samples are face samples whose recognition results obtained after supplementary light are smaller than the preset threshold.
  • the face samples whose recognition results after filling light are less than the preset threshold are screened out. These face samples can be considered as samples with unsatisfactory filling light effect, and will be screened.
  • the obtained face samples are determined as training samples, that is, the training samples are determined from the face samples according to the recognition results.
  • the specific value corresponding to the preset threshold may be set according to the actual working condition, which is not limited in this embodiment.
  • S503 Use the second face sample image obtained before each training sample fills the light as a historical face image to optimize the optimal combination of regression parameters.
  • the optimal regression parameter combination in the embodiment shown in FIG. 4 is further optimized by using the selected training samples, so as to verify and optimize the determined preset supplementary light model.
  • the optimal regression parameter combination is re-determined according to each step of the embodiment shown in FIG. 4, so as to optimize the previously determined optimal regression parameter combination, so as to achieve the verification and optimization prediction. Set the purpose of the fill light model.
  • the face recognition fill light method provided by the embodiment of the present application further includes a step of optimizing the optimal regression parameter combination to verify the optimization of the preset fill light model after controlling the fill light to fill light according to the current fill light intensity.
  • the recognition results corresponding to multiple face samples before and after fill light are obtained, and then the face samples smaller than the preset threshold are determined from the recognition results after fill light as training samples, and then the The second face sample image obtained before each training sample fill light is used as a historical face image, and the optimal regression parameter combination is re-determined to optimize the previous optimal regression parameter combination, thereby achieving the purpose of verifying and optimizing the preset fill light model , so that the current fill light intensity determined based on the preset fill light model has higher accuracy and validity, which is beneficial to the popularization and use of the face recognition fill light method in actual working conditions.
  • FIG. 7 is a schematic structural diagram of a face recognition supplementary light device according to an embodiment of the present application.
  • the face recognition supplementary light device 600 provided in this embodiment includes:
  • the first acquiring module 601 is used to acquire the face image to be recognized.
  • the first processing module 602 is configured to determine the parameters of the image to be processed according to the face image to be recognized and a preset image processing model.
  • the parameters of the image to be processed include the current brightness of the face image to be recognized, the first distance, and the first angle, the first distance represents the distance between the face to be recognized and the face recognition device, and the first angle represents the face to be recognized. An angle off the face recognition device.
  • the second processing module 603 is configured to, based on the preset supplementary light model, determine the current supplementary light intensity according to the current brightness, the first distance and the first angle of the face image to be recognized, so as to control the supplementary light to perform supplementation according to the current supplementary light intensity Light.
  • FIG. 8 is a schematic structural diagram of another face recognition supplementary light device provided by an embodiment of the present application.
  • the face recognition supplementary light device 600 provided in this embodiment further includes:
  • the second obtaining module 604 is configured to obtain a plurality of historical face images and respective historical recognition results corresponding to each historical face image.
  • the third processing module 605 is configured to generate a respective multiple linear regression model according to each historical face image and the corresponding historical recognition result based on the preset multiple linear regression model.
  • the fourth processing module 606 is configured to determine the optimal regression parameter combination according to all the multiple linear regression models, and determine the preset supplementary light model according to the preset multiple linear regression model and the optimal regression parameter combination.
  • the third processing module 605 is specifically used for:
  • each historical image parameter includes the current brightness of the historical face image, the second distance and the second angle, the second distance represents the distance between the historical face and the face recognition device, and the second angle represents the deviation of the historical face from the human face.
  • the angle of the face recognition device includes the current brightness of the historical face image, the second distance and the second angle, the second distance represents the distance between the historical face and the face recognition device, and the second angle represents the deviation of the historical face from the human face.
  • the fourth processing module 606 is further configured to:
  • the preset multiple linear regression model with the corresponding regression parameters configured is determined as the preset supplementary light model.
  • the second processing module 603 is further configured to:
  • the first processing module 602 is specifically configured to:
  • the current brightness of the face image to be recognized is determined according to a preset grayscale algorithm in the preset image processing model, and the preset grayscale algorithm is used to represent the distribution function of each grayscale value corresponding to all pixels of the face image to be recognized;
  • the first angle is determined according to a preset image algorithm in the preset image processing model.
  • the first processing module 602 is further configured to:
  • the recognition frame area refers to the face recognition device performing face recognition. the area of the preset frame;
  • For each first face sample image determine the ratio of the face area of the first face sample image to the area of the recognition frame, and obtain the distance between the face corresponding to the first face sample image and the face recognition device data;
  • a distance mapping relationship is generated according to each ratio and corresponding distance data, and the distance mapping relationship is used to characterize the preset distance algorithm.
  • the face recognition supplementary light device 600 further includes: a fifth processing module; the fifth processing module is used for:
  • a training sample is determined from the face sample according to the recognition result, and the training sample is a face sample whose recognition result obtained after fill light is less than a preset threshold;
  • the second face sample image obtained before each training sample is filled with light is used as the historical face image to optimize the optimal combination of regression parameters.
  • the second processing module 603 is further configured to:
  • the device for supplementing light by face recognition provided by the above embodiments can be used to execute each step in the method for supplementary light by face recognition provided by any one of the above embodiments.
  • the specific implementation methods and technical effects are similar, and will not be repeated here. .
  • modules division is only a logical function division, and there may be other division manners in actual implementation.
  • multiple modules can be combined or can be integrated into another system.
  • the coupling between the various modules may be implemented through some interfaces, which are usually electrical communication interfaces, but may be mechanical interfaces or other forms of interfaces.
  • modules described as separate components may or may not be physically separate, and may be located in one place or distributed in different locations on the same or different devices.
  • FIG. 9 is a schematic structural diagram of a face recognition device according to an embodiment of the present application.
  • the face recognition device 700 may include: a fill light 701 , at least one processor 702 and a memory 703 .
  • FIG. 9 shows a processor as an example.
  • the fill light 701 is used for fill light.
  • the connection between the fill light 701 and the face recognition device 700 is not limited.
  • the memory 703 is used to store the program of the processor 702 .
  • the program may include program code, and the program code includes computer operation instructions.
  • the memory 703 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory.
  • the processor 702 is configured to execute the computer program stored in the memory 703, so as to implement the steps in the face recognition and light supplementation methods in the above method embodiments.
  • the processor 702 may be a central processing unit (central processing unit, referred to as CPU), or a specific integrated circuit (application specific integrated circuit, referred to as ASIC), or is configured to implement one or more of the embodiments of the present application. multiple integrated circuits.
  • CPU central processing unit
  • ASIC application specific integrated circuit
  • the memory 703 may be independent or integrated with the processor 702 .
  • the face recognition device 700 may also include:
  • the bus 704 is used to connect the processor 702 and the memory 703 .
  • the bus may be an industry standard architecture (abbreviated as ISA) bus, a peripheral component (PCI) bus, or an extended industry standard architecture (EISA) bus or the like. Buses can be divided into address bus, data bus, control bus, etc., but it does not mean that there is only one bus or one type of bus.
  • ISA industry standard architecture
  • PCI peripheral component
  • EISA extended industry standard architecture
  • the memory 703 and the processor 702 can communicate through an internal interface.
  • the present application also provides a computer-readable storage medium
  • the computer-readable storage medium may include: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM) ), magnetic disks or CDs and other media that can store program codes, specifically, a computer program is stored in the computer-readable storage medium, when at least one processor of the above-mentioned face recognition device executes the computer program, the face The recognition device executes each step of the methods for face recognition and light supplement provided by the above-mentioned various embodiments.
  • Embodiments of the present application further provide a computer program product, where the computer program product includes a computer program, and the computer program is stored in a readable storage medium.
  • At least one processor of the face recognition device can read the computer program from the readable storage medium, and the at least one processor executes the computer program so that the device implements each step of the face recognition fill light method provided by the above-mentioned various embodiments.
  • An embodiment of the present application further provides a face recognition system, including any possible face recognition device provided in the above-mentioned embodiments, and an access control device that is communicatively connected to the face recognition device.
  • the access control device is used to select whether to open the access control according to the corresponding recognition result of the face recognition device.

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Abstract

L'invention concerne un procédé et un appareil d'ajout de lumière pour la reconnaissance faciale, ainsi qu'un dispositif de reconnaissance faciale et un système associé. Le procédé consiste à : acquérir une image faciale devant être soumise à une reconnaissance ; à l'aide d'un modèle de traitement d'image prédéfini et en fonction de ladite image faciale, obtenir un paramètre d'image à traiter ; et enfin, déterminer l'intensité d'ajout de lumière actuelle d'après un modèle d'ajout de lumière prédéfini et le paramètre d'image à traiter, puis amener une lampe d'ajout de lumière à réaliser un ajout de lumière en fonction de l'intensité d'ajout de lumière actuelle. Par conséquent, l'intensité d'ajout de lumière actuelle est déterminé en temps réel en fonction d'une image faciale devant être soumise à une reconnaissance afin de réaliser un ajout de lumière adaptatif, de façon à ce qu'une politique d'ajout de lumière fournie puisse satisfaire l'exigence réelle de l'intensité d'ajout de lumière dans des conditions de travail réelles, ce qui est différent d'une politique d'ajout de lumière dans l'état de la technique consistant à définir une intensité d'ajout de lumière fixe ou à réaliser un ajout de lumière à une valeur fixe, ce qui permet d'améliorer efficacement la précision d'ajout de lumière. De plus, le procédé d'ajout de lumière pour la reconnaissance faciale n'a pas besoin d'être basé sur un capteur photosensible, et ne présente pas les problèmes d'une erreur d'ajout de lumière, d'un temps consommé plus long et d'une perte de ressource matérielle, de façon à pouvoir résoudre fondamentalement le problème d'un taux de réussite inférieur de reconnaissance faciale provoqué par une lumière ambiante insuffisante.
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