WO2019100608A1 - Dispositif de capture vidéo, procédé de reconnaissance de visage, système, et support d'informations lisible par ordinateur - Google Patents

Dispositif de capture vidéo, procédé de reconnaissance de visage, système, et support d'informations lisible par ordinateur Download PDF

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Publication number
WO2019100608A1
WO2019100608A1 PCT/CN2018/076140 CN2018076140W WO2019100608A1 WO 2019100608 A1 WO2019100608 A1 WO 2019100608A1 CN 2018076140 W CN2018076140 W CN 2018076140W WO 2019100608 A1 WO2019100608 A1 WO 2019100608A1
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Prior art keywords
face
point
video data
image
face image
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PCT/CN2018/076140
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English (en)
Chinese (zh)
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陈林
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平安科技（深圳）有限公司
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    • 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
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/161—Detection; Localisation; Normalisation
    • G06V40/166—Detection; Localisation; Normalisation using acquisition arrangements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • 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
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships

Definitions

  • the present application relates to the field of image processing technologies, and in particular, to a camera device, a method and system for recognizing a face, and a computer readable storage medium.
  • the existing 1:N dynamic face recognition system generally uses one server to connect one or more network cameras, and the server collects video data from the camera through the network, and performs face recognition on the video data, such a centralized analysis scheme
  • the computing pressure of the server is very large, especially when the number of cameras is large, usually one server can not meet the demand, the server array needs to be built, and there are high requirements in terms of power consumption and heat dissipation; in addition, since the video data needs to be transmitted from the camera To the server, the pressure on the network is also relatively large, and this pressure will rise as the resolution and quality of the camera increase.
  • the purpose of the present application is to provide an image capturing apparatus, a method and system for recognizing a face, and a computer readable storage medium, which aim to alleviate the calculation pressure of the server in face recognition and reduce the network transmission pressure.
  • an image pickup apparatus including a memory and a processor connected to the memory, wherein the memory stores a processing system operable on the processor, the processing The system implements the following steps when executed by the processor:
  • Detection step performing face detection on the video data to obtain a face image
  • Tracking step tracking the face image to obtain a sequence of face images
  • Image quality scoring step performing image quality scoring on the sequence of face images, and obtaining a preset number of face images with the highest score;
  • Feature point positioning step performing feature point positioning on a preset number of face images with a higher score, and correcting based on the positioned face image;
  • Feature vector output step inputting the corrected face image into a depth neural network model generated by pre-training, and acquiring the output face feature vector;
  • Transmission step transmitting the face feature vector to the server to perform a step of performing a comparison operation with the face image in the sample in the face image sample library.
  • the present application further provides a method for face recognition, and the method for face recognition includes:
  • the corrected face image is input into a depth neural network model generated by pre-training, and the output face feature vector is obtained;
  • S6 Send the face feature vector to the server to perform a step of performing a comparison operation with the face image in the sample in the face image sample library.
  • the present application further provides a system for face recognition, the system for face recognition comprising:
  • a detecting module configured to perform face detection on the video data to obtain a face image
  • a tracking module for tracking a face image to obtain a sequence of face images
  • a scoring module configured to perform image quality scoring on the sequence of face images, and obtain a preset number of face images with a higher score
  • a correction module configured to perform feature point positioning on a preset number of face images that are scored first, and perform correction based on the positioned face image
  • An input module configured to input the corrected face image into a depth neural network model generated by pre-training, and obtain an output face feature vector
  • the sending module is configured to send the face feature vector to the server to trigger an operation of comparing the face image in the sample in the face image sample library.
  • the application further provides a computer readable storage medium having a processing system stored thereon, the processing system being implemented by a processor to implement the steps:
  • Detection step performing face detection on the video data to obtain a face image
  • Tracking step tracking the face image to obtain a sequence of face images
  • Image quality scoring step performing image quality scoring on the sequence of face images, and obtaining a preset number of face images with the highest score;
  • Feature point positioning step performing feature point positioning on a preset number of face images with a higher score, and correcting based on the positioned face image;
  • Feature vector output step inputting the corrected face image into a depth neural network model generated by pre-training, and acquiring the output face feature vector;
  • Transmission step transmitting the face feature vector to the server to perform a step of performing a comparison operation with the face image in the sample in the face image sample library.
  • the beneficial effects of the present application are: the processing of one video data per camera device of the present application, in addition to collecting video, the camera device can perform face detection, tracking, image quality scoring, feature point localization and input depth neural network model on the video. In the middle, the face feature vector is obtained, and finally only the face feature vector is transmitted to the server.
  • the calculation pressure of the server can be greatly reduced, and the server array does not need to be built, and the network transmission can be reduced to a large extent. Pressure, and network transmission pressure does not rise with the resolution and image quality of the camera.
  • FIG. 1 is a schematic diagram of an optional application environment of each embodiment of the present application.
  • FIG. 2 is a schematic diagram of a hardware architecture of an embodiment of the camera device of FIG. 1;
  • FIG. 3 is a schematic flowchart diagram of an embodiment of a method for recognizing a face of an applicant.
  • FIG. 1 it is a schematic diagram of an application environment of a preferred embodiment of the method for face recognition of the present applicant.
  • the application environment diagram includes a camera device 1 and a server 2.
  • the plurality of imaging apparatuses 1 can perform data interaction with the server 2 through suitable technologies such as a network and a near field communication technology, respectively.
  • the server 2 may be a single network server, a server group composed of a plurality of network servers, or a cloud-based cloud composed of a large number of hosts or network servers, wherein the cloud computing is a kind of distributed computing, and is a group of loosely coupled computers. A set of super virtual computers.
  • the camera device 1 is a common electronic product that includes a camera and can dynamically acquire images, and can automatically perform numerical calculation and/or information processing according to preset or stored instructions.
  • the image capturing apparatus 1 may include, but is not limited to, a memory 11, a processor 12, a network interface 13, and a camera 14 that are communicably connected to each other through a system bus.
  • the memory 11 is stored and processed.
  • Each camera device 1 includes a processor (the processor is an nvidia tx2 chip for processing images), and the nvidia tx2 chip can be connected to the camera device 1 via a usb or csi or a network interface to run the processing system.
  • the imaging device 1 and the server 2 are connected by a network, and the server 2 stores a database of human face image samples.
  • the camera device 1 is installed in a specific place (for example, an office place and a monitoring area), and a video is captured in real time for a target entering the specific place, and the processor processes the video to obtain a face feature vector, and then only sends the face feature vector through the network.
  • the server 2 performs comparison based on the face image sample library to implement face recognition.
  • the memory 11 includes a memory and at least one type of readable storage medium.
  • the memory provides a cache for the operation of the camera device 1;
  • the readable storage medium may be, for example, a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM).
  • a non-volatile storage medium such as a read only memory (ROM), an electrically erasable programmable read only memory (EEPROM), a programmable read only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, or the like.
  • the readable storage medium may be an internal storage unit of the camera 1, such as a hard disk of the camera 1; in other embodiments, the non-volatile storage medium may also be external to the camera 1.
  • the storage device is, for example, a plug-in hard disk provided on the camera 1, a smart memory card (SMC), a Secure Digital (SD) card, a flash card, or the like.
  • the readable storage medium of the memory 11 is generally used to store an operating system installed in the image pickup apparatus 1 and various types of application software, such as program codes of the processing system in an embodiment of the present application. Further, the memory 11 can also be used to temporarily store various types of data that have been output or are to be output.
  • the processor 12 is configured to run program code or process data stored in the memory 11, such as running a processing system or the like.
  • the network interface 13 may comprise a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the camera device 1 and other electronic devices.
  • the network interface 13 is mainly used to connect the camera device 1 to the server 2, and establish a data transmission channel and a communication connection between the camera device 1 and the server 2.
  • the processing system is stored in the memory 11 and includes at least one computer readable instruction stored in the memory 11, the at least one computer readable instruction being executable by the processor 12 to implement the methods of various embodiments of the present application;
  • the at least one computer readable instruction can be classified into different logic modules depending on the functions implemented by its various parts.
  • Detection step performing face detection on the video data to obtain a face image
  • Face detection is performed on each frame image in the video data based on the features of the face, and there may be one or more faces in each frame image, or no face, and after face detection, the image may be extracted from the image. Face image.
  • the face image is an image including only a face region (no other background), and the face region can be large or small.
  • the face region is small, and for a close-up shot of a face image,
  • the face area is large.
  • the face area is a minimum area including a human face, and is preferably a rectangular area including a human face. Of course, it may be an area including a human face of other shapes, such as a circular area, and is not limited thereto.
  • Tracking step tracking the face image to obtain a sequence of face images
  • the similarity of the adjacent two frames of the face image can be calculated to implement face tracking.
  • the similarity of the face may be calculated according to the X and Y coordinate values of the center point of the face region in the adjacent two frames of the face image; in other embodiments, the face image of the adjacent two frames may be used.
  • the X, Y coordinate values of the center point of the face region in the face region, and the height H and the width W value of the face region are calculated, and the similarity of the faces in the adjacent two frames of the face image is calculated.
  • the face tracking is performed based on the similarity of the faces in the adjacent two frames of the face image, and a sequence of face images of the same person is obtained, and two or more persons appearing in the face image may also be respectively obtained.
  • Image quality scoring step performing image quality scoring on the sequence of face images, and obtaining a preset number of face images with the highest score;
  • the quality of each face image in the series is scored according to the gradient values and coordinate values of predetermined points in the face image.
  • the predetermined points include an eye point, a nose point and a mouth point
  • the gradient value of the predetermined point is a mean gradient
  • the average gradient refers to a boundary of a predetermined point of the face image or a gray near the sides of the shadow line.
  • the coordinate values of the predetermined points include at least the x-axis of the eye point and the nose point.
  • Feature point positioning step performing feature point positioning on a preset number of face images with a higher score, and correcting based on the positioned face image;
  • the scoring results are arranged in descending order, that is, the face image is arranged in front of the face image, and the sequence is selected from the arranged sequence.
  • the preset number of face images of the top score for example, 7 face images are selected.
  • the feature points include at least an eye feature point, a mouth feature point, and a nose feature point, and are corrected based on the face image after the feature point is positioned.
  • Feature vector output step inputting the corrected face image into a depth neural network model generated by pre-training, and acquiring the output face feature vector;
  • Transmission step transmitting the face feature vector to the server to perform a step of performing a comparison operation with the face image in the sample in the face image sample library.
  • the corrected face image is input into a depth neural network model generated by pre-training, and is calculated by a deep neural network model, and then the face feature vector of each face image is output, and then the camera device only The face feature vector is transmitted to the server for 1:N dynamic recognition.
  • each camera device in the embodiment processes one channel of video data, and the camera device can perform face detection, tracking, image quality scoring, feature point location, and input depth neural network model in addition to video capture.
  • the face feature vector is obtained, and finally only the face feature vector is transmitted to the server.
  • the calculation pressure of the server can be greatly reduced, and the server array does not need to be built, and the network transmission can be reduced to a large extent. Pressure, and network transmission pressure does not rise with the resolution and image quality of the camera.
  • the method further includes:
  • the format of the video data is converted into a format capable of face detection
  • the video data is decoded and the format of the video data is converted into a format capable of face detection.
  • the camera device may compress the video data after the video data is collected.
  • the compressed video data may be non-real-time compressed or compressed in real time according to the real-time performance.
  • This embodiment is preferably real-time compression.
  • the captured video data may be subjected to lossy compression according to actual conditions, and the compression ratio is a predetermined ratio, preferably 5:1.
  • Video compression algorithms include M-JPEG (Motion-Join Photographic Experts Group), Mpeg (Moving Pictures Experts Group), H.264, Wavelet (Wavelet Compression), JPEG 2000, AVS compression, etc., through the above compression algorithm to obtain compressed data. Before the face detection, it can be analyzed whether the video data is compressed.
  • the format is a compressed format, and if it is further processed, for example, the camera is compressed by M-JPEG, and the format is YCrCB. , you need to convert the video data in YCrCB format to RGB format so that face detection can be performed.
  • the tracking step specifically includes:
  • Face tracking is performed based on the similarity of faces in adjacent two frames of face images.
  • the similarity calculation step includes:
  • the S i,j is a similarity, and the w x , w y , w w , w h are the x-direction distance, the y-direction distance, the width difference, and the height difference of the adjacent two frames of the face i and the face j, respectively.
  • the face in the adjacent two frames of the face image is determined to be the same person's face.
  • the image quality scoring step specifically includes: each of the series according to a gradient value and a coordinate value of a predetermined point in the face image. The quality of the face image is scored.
  • the predetermined point includes an eye point, a nose point, and a mouth point
  • the gradient value is an average gradient of an eye point, a nose point, and a mouth point
  • the eye point includes a left eye point and The right eye point
  • the mouth point includes a left mouth corner point and a right mouth corner point
  • the image quality score is calculated as:
  • x_LeftEye represents the X coordinate of the left and right eyeballs
  • x_Nose represents the X coordinate of the tip of the nose
  • grad is the average gradient of the eye point, nose point and mouth point.
  • the coordinates of the eye point, the nose point, and the mouth point in the face are selected to score the quality of the face image, and the face image can be objectively and accurately evaluated to obtain a person with a high score.
  • the face image is convenient for subsequent correction and other processing.
  • FIG. 3 is a schematic flowchart of an embodiment of a method for recognizing a face of an applicant, where the method includes the following steps:
  • Step S1 performing face detection on the video data to obtain a face image
  • Face detection is performed on each frame image in the video data based on the features of the face, and there may be one or more faces in each frame image, or no face, and after face detection, the image may be extracted from the image. Face image.
  • the face image is an image including only a face region (no other background), and the face region can be large or small.
  • the face region is small, and for a close-up shot of a face image,
  • the face area is large.
  • the face area is a minimum area including a human face, and is preferably a rectangular area including a human face. Of course, it may be an area including a human face of other shapes, such as a circular area, and is not limited thereto.
  • Step S2 tracking the face image to obtain a sequence of face images
  • the similarity of the adjacent two frames of the face image can be calculated to implement face tracking.
  • the similarity of the face may be calculated according to the X and Y coordinate values of the center point of the face region in the adjacent two frames of the face image; in other embodiments, the face image of the adjacent two frames may be used.
  • the X, Y coordinate values of the center point of the face region in the face region, and the height H and the width W value of the face region are calculated, and the similarity of the faces in the adjacent two frames of the face image is calculated.
  • the face tracking is performed based on the similarity of the faces in the adjacent two frames of the face image, and a sequence of face images of the same person is obtained, and two or more persons appearing in the face image may also be respectively obtained.
  • Step S3 performing image quality scoring on the sequence of face images, and obtaining a preset number of face images with a higher score
  • the quality of each face image in the series is scored according to the gradient values and coordinate values of predetermined points in the face image.
  • the predetermined points include an eye point, a nose point and a mouth point
  • the gradient value of the predetermined point is a mean gradient
  • the average gradient refers to a boundary of a predetermined point of the face image or a gray near the sides of the shadow line.
  • the coordinate values of the predetermined points include at least the x-axis of the eye point and the nose point.
  • Step S4 performing feature point positioning on the preset number of face images that are scored first, and correcting based on the positioned face image;
  • the scoring results are arranged in descending order, that is, the face image is arranged in front of the face image, and the sequence is selected from the arranged sequence.
  • the preset number of face images of the top score for example, 7 face images are selected.
  • the feature points include at least an eye feature point, a mouth feature point, and a nose feature point, and are corrected based on the face image after the feature point is positioned.
  • Step S5 the corrected face image is input into the depth neural network model generated by the pre-training, and the output face feature vector is obtained;
  • step S6 the face feature vector is sent to the server to perform a step of performing a comparison operation with the face image in the sample in the face image sample library.
  • the corrected face image is input into a depth neural network model generated by pre-training, and is calculated by a deep neural network model, and then the face feature vector of each face image is output, and then the camera device only The face feature vector is transmitted to the server for 1:N dynamic recognition.
  • each camera device in the embodiment processes one channel of video data, and the camera device can perform face detection, tracking, image quality scoring, feature point location, and input depth neural network model in addition to video capture.
  • the face feature vector is obtained, and finally only the face feature vector is transmitted to the server.
  • the calculation pressure of the server can be greatly reduced, and the server array does not need to be built, and the network transmission can be reduced to a large extent. Pressure, and network transmission pressure does not rise with the resolution and image quality of the camera.
  • the method further includes:
  • the format of the video data is converted into a format capable of face detection
  • the video data is decoded and the format of the video data is converted into a format capable of face detection.
  • the camera device may compress the video data after the video data is collected.
  • the compressed video data may be non-real-time compressed or compressed in real time according to the real-time performance.
  • This embodiment is preferably real-time compression.
  • the captured video data may be subjected to lossy compression according to actual conditions, and the compression ratio is a predetermined ratio, preferably 5:1.
  • Video compression algorithms include M-JPEG (Motion-Join Photographic Experts Group), Mpeg (Moving Pictures Experts Group), H.264, Wavelet (Wavelet Compression), JPEG 2000, AVS compression, etc., obtains compressed video data through the above compression algorithm. Before the face detection, it can be analyzed whether the video data is compressed.
  • the format is a compressed format, and if it is further processed, for example, the camera is compressed by M-JPEG, and the format is YCrCB. , you need to convert the video data in YCrCB format to RGB format so that face detection can be performed.
  • the step S2 specifically includes:
  • Face tracking is performed based on the similarity of faces in adjacent two frames of face images.
  • the similarity calculation step includes:
  • the S i,j is a similarity, and the w x , w y , w w , w h are the x-direction distance, the y-direction distance, the width difference, and the height difference of the adjacent two frames of the face i and the face j, respectively.
  • the face in the adjacent two frames of the face image is determined to be the same person's face.
  • the step S3 specifically includes:
  • the quality of each face image in the series is scored according to the gradient values and coordinate values of the predetermined points in the face image.
  • the predetermined point includes an eye point, a nose point, and a mouth point
  • the gradient value is an average gradient of an eye point, a nose point, and a mouth point
  • the eye point includes a left eye point and The right eye point
  • the mouth point includes a left mouth corner point and a right mouth corner point
  • the image quality score is calculated as:
  • x_LeftEye represents the X coordinate of the left and right eyeballs
  • x_Nose represents the X coordinate of the tip of the nose
  • grad is the average gradient of the eye point, nose point and mouth point.
  • the coordinates of the eye point, the nose point, and the mouth point in the face are selected to score the quality of the face image, and the face image can be objectively and accurately evaluated to obtain a person with a high score.
  • the face image is convenient for subsequent correction and other processing.
  • the present application also provides a computer readable storage medium having stored thereon a processing system, the processing system being executed by a processor to implement the steps of the method of face recognition described above.
  • the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better.
  • Implementation Based on such understanding, the technical solution of the present application, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk,
  • the optical disc includes a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in various embodiments of the present application.

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Abstract

La présente invention concerne un dispositif de capture vidéo, un procédé de reconnaissance de visage, un système, et un support d'informations lisible par ordinateur. Le procédé de reconnaissance de visage comprend : l'exécution d'une détection de visage sur des données vidéo pour obtenir une image de visage ; le suivi de l'image de visage et l'acquisition d'une séquence d'images de visage ; la réalisation d'une évaluation de qualité d'image sur la séquence d'images de visage, et l'acquisition d'un nombre prédéfini d'images de visage de première qualité ; l'exécution d'un positionnement de point caractéristique sur le nombre prédéfini d'images de visage de première qualité et l'exécution d'un étalonnage sur la base des images de visage positionnées ; l'entrée des images de visage étalonnées dans un modèle de réseau neuronal profond qui est généré par apprentissage préalable, et l'acquisition d'un vecteur de caractéristique de visage sorti ; et l'envoi du vecteur de caractéristique de visage à un serveur afin d'exécuter une opération de comparaison avec des images de visage dans des échantillons dans une bibliothèque d'échantillons d'images de visage. Selon la présente invention, la charge de calculs du serveur durant une reconnaissance de visage peut être réduite et la charge de transmissions dans le réseau peut être diminuée.
PCT/CN2018/076140 2017-11-21 2018-02-10 Dispositif de capture vidéo, procédé de reconnaissance de visage, système, et support d'informations lisible par ordinateur WO2019100608A1 (fr)

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CN110610533A (zh) * 2019-08-07 2019-12-24 重庆灵翎互娱科技有限公司 一种基于人脸三维网格模型进行表情捕捉的方法和设备
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