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WO2019033572A1 - Procédé de détection de situation de visage bloqué, dispositif et support d'informations - Google Patents

Procédé de détection de situation de visage bloqué, dispositif et support d'informations Download PDF

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Publication number
WO2019033572A1
WO2019033572A1 PCT/CN2017/108751 CN2017108751W WO2019033572A1 WO 2019033572 A1 WO2019033572 A1 WO 2019033572A1 CN 2017108751 W CN2017108751 W CN 2017108751W WO 2019033572 A1 WO2019033572 A1 WO 2019033572A1
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face
lip
image
eye
model
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Chinese (zh)
Inventor
陈林
张国辉
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Ping An Technology Shenzhen Co Ltd
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Ping An Technology Shenzhen Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features

Definitions

  • the present application relates to the field of computer vision processing technologies, and in particular, to a face occlusion detection method, apparatus, and computer readable storage medium.
  • Face recognition is a biometric recognition technology based on human facial feature information for identity authentication.
  • the detected face is matched and recognized by collecting an image or a video stream containing a face and detecting and tracking the face in the image.
  • the application field of face recognition is very extensive, and plays a very important role in many fields such as financial payment, access control, and identification, which brings great convenience to people's lives.
  • the general product in the industry judges that the face occlusion is determined by the deep learning training method, but the judgment method has high requirements on the sample size, and if the occlusion is predicted by the deep learning method, the calculation amount is large and the speed is relatively slow.
  • the present application provides a face occlusion detection method, apparatus, and computer readable storage medium, the main purpose of which is to quickly detect a face occlusion situation in a real-time facial image.
  • the present application provides an electronic device, including: a memory, a processor, and an imaging device, wherein the memory includes a face occlusion detection program, and the face occlusion detection program is executed by the processor Implement the following steps:
  • Image acquisition step acquiring a real-time image captured by the camera device, and extracting a real-time face image from the real-time image by using a face recognition algorithm;
  • a feature point recognition step inputting the real-time facial image into a pre-trained facial average model, and using the facial average model to identify t facial feature points from the real-time facial image;
  • a feature region determining step determining an eye region and a lip region according to position information of the t facial feature points, and inputting the eye region and the lip region into an eye part class model of a pre-trained face, a face
  • the lip part type model determines the authenticity of the eye area and the lip area, and determines whether the face in the real-time image is occluded according to the judgment result.
  • the following steps are further implemented:
  • a determining step determining an eye part class model of the face, a lip part class model of the face Whether the judgment results of the eye area and the lip area are true.
  • the following steps are further implemented:
  • the present application further provides a method for detecting a face occlusion, the method comprising:
  • Image acquisition step acquiring a real-time image captured by the camera device, and extracting a real-time face image from the real-time image by using a face recognition algorithm;
  • a feature point recognition step inputting the real-time facial image into a pre-trained facial average model, and using the facial average model to identify t facial feature points from the real-time facial image;
  • a feature region determining step determining an eye region and a lip region according to position information of the t facial feature points, and inputting the eye region and the lip region into an eye part class model of a pre-trained face, a face
  • the lip part type model determines the authenticity of the eye area and the lip area, and determines whether the face in the real-time image is occluded according to the judgment result.
  • the method further comprises:
  • the determining step is: determining whether the judgment result of the eye part class model and the lip part class model of the human face on the eye region and the lip region is true.
  • the method further comprises:
  • the present application further provides a computer readable storage medium including a face occlusion detection program, where the face occlusion detection program is executed by a processor, as described above Any of the steps in the face occlusion detection method described.
  • the face occlusion detection method, the electronic device and the computer readable storage medium proposed by the present application identify the facial feature points in the real-time facial image by inputting the real-time facial image into the face averaging model, and use the eye part class of the human face
  • the lip model of the model and the face determines the authenticity of the eye region and the lip region determined by the facial feature point, and determines whether the face in the real-time facial image occurs according to the authenticity of the eye region and the lip region. Occlusion.
  • FIG. 1 is a schematic diagram of an application environment of a preferred embodiment of a method for detecting a face occlusion of an applicant
  • FIG. 2 is a block diagram of a face occlusion detection program of FIG. 1;
  • FIG. 3 is a flow chart of a preferred embodiment of the applicant's face occlusion detection method.
  • the present application provides a face occlusion detection method applied to an electronic device 1 .
  • FIG. 1 it is a schematic diagram of an application environment of a preferred embodiment of the applicant's face occlusion detection method.
  • the electronic device 1 may be a terminal device having a computing function, such as a server, a smart phone, a tablet computer, a portable computer, or a desktop computer.
  • a computing function such as a server, a smart phone, a tablet computer, a portable computer, or a desktop computer.
  • the electronic device 1 includes a processor 12, a memory 11, an imaging device 13, a network interface 14, and a communication bus 15.
  • the camera device 13 is installed in a specific place, such as an office place and a monitoring area, and real-time images are taken in real time for the target entering the specific place, and the captured real-time image is transmitted to the processor 12 through the network.
  • Network interface 14 may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface).
  • Communication bus 15 is used to implement connection communication between these components.
  • the memory 11 includes at least one type of readable storage medium.
  • the at least one type of readable storage medium may be a non-volatile storage medium such as a flash memory, a hard disk, a multimedia card, a card type memory, or the like.
  • the readable storage medium may be an internal storage unit of the electronic device 1, such as a hard disk of the electronic device 1.
  • the readable storage medium may also be an external memory of the electronic device 1, such as a plug-in hard disk equipped on the electronic device 1, a smart memory card (SMC), Secure Digital (SD) card, Flash Card, etc.
  • SMC smart memory card
  • SD Secure Digital
  • the readable storage medium of the memory 11 is generally used to store a face occlusion detection program 10 installed on the electronic device 1, a face image sample library, a human eye sample library, and a human lip sample.
  • the memory 11 can also be used to temporarily store data that has been output or is about to be output.
  • the processor 12 may be a Central Processing Unit (CPU), microprocessor or other data processing chip for running program code or processing data stored in the memory 11, such as performing face occlusion Test procedure 10, etc.
  • CPU Central Processing Unit
  • microprocessor or other data processing chip for running program code or processing data stored in the memory 11, such as performing face occlusion Test procedure 10, etc.
  • Figure 1 shows only the electronic device 1 with components 11-15, but it should be understood that not all illustrated components may be implemented, and more or fewer components may be implemented instead.
  • the electronic device 1 may further include a user interface, and the user interface may include an input unit such as a keyboard, a voice input device such as a microphone, and the like.
  • the device for voice recognition function, voice output device such as audio, earphone, etc. optionally the user interface may also include a standard wired interface, a wireless interface.
  • the electronic device 1 may further include a display, which may also be appropriately referred to as a display screen or a display unit.
  • a display may also be appropriately referred to as a display screen or a display unit.
  • it may be an LED display, a liquid crystal display, a touch liquid crystal display, and an Organic Light-Emitting Diode (OLED) touch sensor.
  • the display is used to display information processed in the electronic device 1 and a user interface for displaying visualizations.
  • the electronic device 1 further comprises a touch sensor.
  • the area provided by the touch sensor for the user to perform a touch operation is referred to as a touch area.
  • the touch sensor described herein may be a resistive touch sensor, a capacitive touch sensor, or the like.
  • the touch sensor includes not only a contact type touch sensor but also a proximity type touch sensor or the like.
  • the touch sensor may be a single sensor or a plurality of sensors arranged, for example, in an array.
  • the area of the display of the electronic device 1 may be the same as or different from the area of the touch sensor.
  • a display is stacked with the touch sensor to form a touch display. The device detects a user-triggered touch operation based on a touch screen display.
  • the electronic device 1 may further include a radio frequency (RF) circuit, a sensor, an audio circuit, and the like, and details are not described herein.
  • RF radio frequency
  • an operating system and a face occlusion detecting program 10 may be included in the memory 11 as a computer storage medium; when the processor 12 executes the face occlusion detecting program 10 stored in the memory 11, Implement the following steps:
  • Image acquisition step acquiring a real-time image captured by the camera device 13, and extracting a real-time face image from the real-time image using a face recognition algorithm.
  • the camera 13 When the camera 13 captures a real-time image, the camera 13 transmits the real-time image to the processor 12.
  • the processor 12 receives the real-time image, it first acquires the size of the image to create a grayscale image of the same size. Converting the acquired color image into a grayscale image and creating a memory space; equalizing the grayscale image histogram, reducing the amount of grayscale image information, speeding up the detection speed, and then loading the training library to detect the person in the image Face, and return an object containing face information, obtain the data of the location of the face, and record the number; finally obtain the area of the avatar and save it, thus completing a real-time facial image extraction process.
  • the face recognition algorithm for extracting the real-time facial image from the real-time image may be a geometric feature-based method, a local feature analysis method, a feature face method, an elastic model-based method, a neural network method, or the like.
  • Feature point identification step input the real-time facial image into a pre-trained facial average model, and use the facial average model to identify t facial feature points from the real-time facial image.
  • a first sample library having n face images, and marking t facial feature points in each face image, the t facial feature points including: t 1 eye feature points representing eye positions, t 2 eye feature points and t 3 lip feature points representing the position of the lips.
  • t 1 eye feature points representing eye positions
  • t 2 eye feature points representing eye positions
  • t 3 lip feature points representing the position of the lips.
  • the feature points constitute a shape feature vector S, and the n shape feature vectors S of the face are obtained.
  • the face feature recognition model is trained by using the t facial feature points to obtain a face average model.
  • the face feature recognition model is an Ensemble of Regression Tress (ERT) algorithm.
  • ERT Regression Tress
  • t represents the cascading sequence number
  • ⁇ t ( ⁇ , ⁇ ) represents the regression of the current stage.
  • Each regression is composed of a number of regression trees, and the purpose of training is to obtain these regression trees.
  • each regression ⁇ t ( ⁇ , ⁇ ) predicts an increment based on the input images I and S(t) Add this increment to the current shape estimate to improve the current model.
  • Each level of regression is based on feature points for prediction.
  • the training data set is: (I1, S1), ..., (In, Sn) where I is the input sample image and S is the shape feature vector composed of the feature points in the sample image.
  • the number of face images in the first sample library is t, assuming that each sample image has 34 feature points, the feature vector x 1 to x 12 represent the abscissa of the eyelid feature point, x 13 to x 14 represent the abscissa of the eye feature point, and x 15 to x 34 represent the abscissa of the lip feature point.
  • Take some feature points of all sample images for example, randomly take 25 feature points out of 34 feature points of each sample picture) to train the first regression tree, and predict the first regression tree and the partial features.
  • the residual of the true value of the point (the weighted average of the 25 feature points taken from each sample picture) is used to train the second tree...
  • the face average model is obtained, and the model file and the sample library are saved in the memory. Since the sample of the training model marks 12 eyelid feature points, 2 eyeball feature points, and 20 lip feature points, the face average model of the trained face can be used to identify 12 eyelid feature points from the face image, 2 eye feature points and 20 lip feature points.
  • the trained facial average model is called from the memory 11
  • the real-time facial image is aligned with the facial average model
  • the feature extraction algorithm is used to search the real-time facial image with the facial average model.
  • the 20 lip feature points are evenly distributed on the lip.
  • a feature region determining step determining an eye region and a lip region according to position information of the t facial feature points, the eye region and the eye region and the lip region according to the position information of the t facial feature points, The eye region and the lip region input an eye part class model of a pre-trained face, a lip part class model of the face, determine the authenticity of the eye region and the lip region, and determine the result according to the judgment result Whether the face in the live image is occluded.
  • a first number of human eye positive sample images and a second number of human eye negative sample images are collected, and local features of each human eye positive sample image and human eye negative sample image are extracted.
  • the human eye positive sample image refers to an eye sample containing a human eye, and the binocular portion can be extracted from the face image sample library as an eye sample.
  • the ocular negative eye sample image refers to an image of a broken eye region, and a plurality of human eye positive sample images and negative sample images form a second sample library.
  • a third number of lip positive sample images and a fourth number of lip negative sample images are collected, extracting local features of each lip positive sample image, lip negative sample image.
  • the lip positive sample image refers to an image containing the human's lips, and the lip portion can be extracted from the face image sample library as a lip positive sample image.
  • the lip negative sample image refers to an image of a person's lip region being defective, or the lip of the image is not the lip of a human (eg, an animal), and the plurality of lip positive sample images and the negative sample image form a third sample bank.
  • the local feature is a Histogram of Oriented Gradient (HOG) feature, which is extracted from a human eye sample image and a lip sample image by a feature extraction algorithm. Since the color information in the sample image is not very effective, it is usually converted into a grayscale image, and the entire image is normalized, the gradient of the horizontal and vertical directions of the image is calculated, and the gradient direction of each pixel position is calculated accordingly. Values to capture outlines, silhouettes, and some texture information, and further weaken the effects of lighting. Then the whole image is divided into individual Cell cells (8*8 pixels), and a gradient direction histogram is constructed for each Cell cell to calculate the local image gradient information and quantize to obtain the feature description vector of the local image region.
  • HOG Oriented Gradient
  • the support vector machine is trained by using the positive and negative sample images and the extracted HOG features in the second sample library and the third sample library to obtain the eye part class model of the face and The lip part class model of the face.
  • an eye region may be determined according to the 12 eyelid feature points and the two eyeball feature points, according to The 20 lip feature points define a lip region, and then the determined eye region and the lip region are input into the eye part class model of the trained face and the lip part class model of the face, and are judged according to the result obtained by the model.
  • the determined authenticity of the eye region and the lip region, that is, the result of the model output may be all false or all true, and may include both true and false.
  • the determining step is: determining whether the judgment result of the eye part class model and the lip part class model of the human face on the eye region and the lip region is true. When the eye part class model of the face and the lip part class model of the face output the result, whether the judgment result contains only true.
  • the eye part type model of the face and the lip part type model of the face are true to the eye area and the lip area, it is determined that the face in the real-time face image is not occluded. That is to say, when the eye region and the lip region determined according to the facial feature points are both the human eye region or the human lip region, it is considered that the human face in the real-time facial image is not blocked.
  • the face in the real-time face image is prompted to be occluded.
  • the face in the real-time facial image is considered to be occluded, prompting the real-time The face in the face image is occluded.
  • the eye area in the image is considered to be occluded
  • the lip part class model output result of the face is false
  • the lip in the image is considered The area is occluded and prompted accordingly.
  • the face recognition is performed after detecting whether the face is occluded, when the face in the real-time face image is occluded, the face in the current face image is occluded and re-acquired.
  • the real-time image captured by the imaging device 13 is subjected to subsequent steps.
  • the electronic device 1 of the embodiment extracts a real-time facial image from a real-time image, and uses a facial average model to identify a facial feature point in the real-time facial image, and uses an eye-part model of the human face and a lip of the human face.
  • the classification model analyzes the eye region and the lip region determined by the facial feature points, and quickly determines whether the face in the current image is occluded according to the authenticity of the eye region and the lip region.
  • the face occlusion detection program 10 can also be partitioned into one or more modules that are stored in the memory 11 and executed by the processor 12 to complete the application.
  • a module as referred to in this application refers to a series of computer program instructions that are capable of performing a particular function. Referring to FIG. 2, it is a block diagram of the face occlusion detection program 10 of FIG.
  • the face occlusion detection program 10 can be divided into: an acquisition module 110, an identification module 120, a determination module 130, and a prompting module 140.
  • the functions or operational steps implemented by the modules 110-140 are similar to the above, and are not described in detail herein, by way of example, for example:
  • the acquiring module 110 is configured to acquire a real-time image captured by the camera device 13 and extract a real-time face image from the real-time image by using a face recognition algorithm;
  • the identification module 120 is configured to input the real-time facial image into a pre-trained facial average model, and use the facial average model to identify t facial feature points from the real-time facial image;
  • the determining module 130 is configured to determine an eye region and a lip region according to position information of the t facial feature points, and input the eye region and the lip region into an eye part class model and a face of the pre-trained face a lip part type model for judging the authenticity of the eye area and the lip area, and determining whether the face in the real-time image is occluded according to the judgment result;
  • the prompting module 140 is configured to prompt the real-time facial image when the eye part class model of the human face and the lip part class model of the human face include the unreality of the judgment result of the eye region and the lip region The face is occluded.
  • this application also provides a face occlusion detection method.
  • this application A flowchart of a first embodiment of a face occlusion detection method. The method can be performed by a device that can be implemented by software and/or hardware.
  • the face occlusion detection method includes:
  • Step S10 Acquire a real-time image captured by the camera device, and extract a real-time face image from the real-time image by using a face recognition algorithm.
  • the camera captures a real-time image
  • the camera transmits the real-time image to the processor.
  • the processor When the processor receives the real-time image, first acquires the size of the image to create a grayscale image of the same size; Color image, converted into gray image, and create a memory space; equalize the gray image histogram, reduce the amount of gray image information, speed up the detection, then load the training library, detect the face in the picture, and return An object containing face information, obtains the data of the location of the face, and records the number; finally obtains the area of the avatar and saves it, thus completing a real-time facial image extraction process.
  • the face recognition algorithm for extracting the real-time facial image from the real-time image may be a geometric feature-based method, a local feature analysis method, a feature face method, an elastic model-based method, a neural network method, or the like.
  • Step S20 input the real-time facial image into a pre-trained facial average model, and use the facial average model to identify t facial feature points from the real-time facial image.
  • a first sample library having n face images, and marking t facial feature points in each face image, the t facial feature points including: t 1 eye feature points representing eye positions, t 2 eye feature points and t 3 lip feature points representing the position of the lips.
  • t 1 eye feature points representing eye positions
  • t 2 eye feature points representing eye positions
  • t 3 lip feature points representing the position of the lips.
  • the feature points constitute a shape feature vector S, and the n shape feature vectors S of the face are obtained.
  • the face feature recognition model is trained by using the t facial feature points to obtain a face average model.
  • the face feature recognition model is an ERT algorithm.
  • the ERT algorithm is expressed as follows:
  • t represents the cascading sequence number
  • ⁇ t ( ⁇ , ⁇ ) represents the regression of the current stage.
  • Each regression is composed of a number of regression trees, and the purpose of training is to obtain these regression trees.
  • each regression ⁇ t ( ⁇ , ⁇ ) predicts an increment based on the input images I and S(t) Add this increment to the current shape estimate to improve the current model.
  • Each level of regression is based on feature points for prediction.
  • the training data set is: (I1, S1), ..., (In, Sn) where I is the input sample image and S is the shape feature vector composed of the feature points in the sample image.
  • the number of face images in the first sample library is t, assuming that each sample image has 34 feature points, the feature vector x 1 to x 12 represent the abscissa of the eyelid feature point, x 13 to x 14 represent the abscissa of the eye feature point, and x 15 to x 34 represent the abscissa of the lip feature point.
  • Take some feature points of all sample images for example, randomly take 25 feature points out of 34 feature points of each sample picture) to train the first regression tree, and predict the first regression tree and the partial features.
  • the residual of the true value of the point (the weighted average of the 25 feature points taken from each sample picture) is used to train the second tree...
  • the face average model is obtained, and the model file and the sample library are saved in the memory. Since the sample of the training model marks 12 eyelid feature points, 2 eyeball feature points, and 20 lip feature points, the face average model of the trained face can be used to identify 12 eyelid feature points from the face image, 2 eye feature points and 20 lip feature points.
  • the trained facial average model is called from the memory, and the real-time facial image is aligned with the facial average model, and the feature extraction algorithm is used to search the real-time facial image with the facial average model.
  • Step S30 determining an eye region and a lip region according to position information of the t facial feature points, and inputting the eye region and the lip region into an eye part type model of a pre-trained face, and a lip of the face
  • the classification model determines the authenticity of the eye region and the lip region, and determines whether the face in the real-time image is occluded according to the judgment result.
  • a first number of human eye positive sample images and a second number of human eye negative sample images are collected, and local features of each human eye positive sample image and human eye negative sample image are extracted.
  • the human eye positive sample image refers to an eye sample containing a human eye, and the binocular portion can be extracted from the face image sample library as an eye sample, and the human eye negative eye sample image refers to an image of a broken eye region, and multiple human eye positive samples.
  • the image and the negative sample image form a second sample library.
  • a third number of lip positive sample images and a fourth number of lip negative sample images are collected, extracting local features of each lip positive sample image, lip negative sample image.
  • the lip positive sample image refers to an image containing the human's lips, and the lip portion can be extracted from the face image sample library as a lip positive sample image.
  • the lip negative sample image refers to an image of a person's lip region being defective, or the lip of the image is not the lip of a human (eg, an animal), and the plurality of lip positive sample images and the negative sample image form a third sample bank.
  • the local feature is a direction gradient histogram (HOG) feature, which is extracted from a human eye sample image and a lip sample image by a feature extraction algorithm. Since the color information in the sample image is not very effective, it is usually converted into a grayscale image, and the entire image is normalized, the gradient of the horizontal and vertical directions of the image is calculated, and the gradient direction of each pixel position is calculated accordingly. Values to capture outlines, silhouettes, and some texture information, and further weaken the effects of lighting. Then the whole image is divided into individual Cell cells (8*8 pixels), and a gradient direction histogram is constructed for each Cell cell to calculate the local image gradient information and quantize to obtain the feature description vector of the local image region. Then the cell cells are combined into a large block.
  • HOG direction gradient histogram
  • the support vector machine classifier is trained to obtain the eye part class model of the face and the lip part class model of the face.
  • an eye region may be determined according to the 12 eyelid feature points and the two eyeball feature points, according to The 20 lip feature points define a lip region, and then the determined eye region and the lip region are input into the eye part class model of the trained face and the lip part class model of the face, and are judged according to the result obtained by the model.
  • the determined authenticity of the eye region and the lip region, that is, the result of the model output may be all false or all true, and may include both true and false.
  • step S40 it is determined whether the judgment result of the eye part class model and the lip part class model of the human face on the eye area and the lip area is true.
  • the eye part class model of the face and the lip part class model of the face output the result, whether the judgment result contains only true.
  • Step S50 when the eye part class model of the face and the lip part class model of the face face the determination result of the eye area and the lip area are true, determining that the face in the real-time face image is not Occlusion occurs. That is to say, when the eye region and the lip region determined according to the facial feature points are both the human eye region or the human lip region, it is considered that the human face in the real-time facial image is not blocked.
  • Step S60 when the eye part class model of the face and the lip part class model of the face face the judgment result of the eye area and the lip area to be untrue, prompting the face in the real-time face image to occur Occlusion.
  • the face in the real-time facial image is considered to be occluded, prompting the real-time The face in the face image is occluded.
  • the eye area in the image is considered to be occluded
  • the lip part class model output result of the face is false
  • the lip in the image is considered The area is occluded and prompted accordingly.
  • the step S50 further includes:
  • the facial facial feature model of the facial feature point is used to identify the key facial feature points in the real-time facial image, and the facial part class model of the human face and the lip partial class model of the human face are used.
  • the eye area and the lip area determined by the point are analyzed, and according to the authenticity of the eye area and the lip area, whether the face in the current image is occluded is detected, and the occlusion of the face in the real-time face image is quickly detected.
  • the embodiment of the present application further provides a computer readable storage medium, the computer readable storage medium
  • the storage medium includes a face occlusion detection program, and when the face occlusion detection program is executed by the processor, the following operations are implemented:
  • Image acquisition step acquiring a real-time image captured by the camera device, and extracting a real-time face image from the real-time image by using a face recognition algorithm;
  • a feature point recognition step inputting the real-time facial image into a pre-trained facial average model, and using the facial average model to identify t facial feature points from the real-time facial image;
  • a feature region determining step determining an eye region and a lip region according to position information of the t facial feature points, and inputting the eye region and the lip region into an eye part class model of a pre-trained face, a face
  • the lip part type model determines the authenticity of the eye area and the lip area, and determines whether the face in the real-time image is occluded according to the judgment result.
  • the determining step is: determining whether the judgment result of the eye part class model and the lip part class model of the human face on the eye region and the lip region is true.
  • the training steps of the eye part class model and the lip part class model of the face include:
  • the human eye negative sample image Using the human eye positive sample image, the human eye negative sample image and its local features to train the support vector machine classifier to obtain the eye part class model of the face;
  • the support vector machine classifier is trained by using the lip positive sample image, the lip negative sample image and its local features to obtain the lip part class model of the face.
  • the training step of the facial average model includes:
  • t facial feature points including: t 1 eye feature points representing eye positions, t 2 eye feature points and t 3 lip feature points representing the position of the lips;
  • the face feature recognition model is trained by using the t facial feature points to obtain a face average model.
  • a disk including a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the methods described in the various embodiments of the present application.
  • a terminal device which may be a mobile phone, a computer, a server, or a network device, etc.

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  • Image Processing (AREA)

Abstract

L'invention concerne un procédé permettant de détecter si un visage est bloqué, un dispositif électronique et un support d'informations lisible par ordinateur. Ledit procédé consiste à : acquérir une image en temps réel capturée par un dispositif de caméra, et extraire une image de visage en temps réel à partir de l'image en temps réel (S10) ; entrer l'image de visage en temps réel dans un modèle de visage moyen, et identifier les points de caractéristique de visage dans l'image de visage en temps réel (S20) ; déterminer, en fonction des informations de position des points de caractéristique de visage, une région d'œil et une région de lèvres, entrer la région d'œil et la région de lèvres dans un modèle de classification d'œil préalablement entraîné du visage et un modèle de classification de lèvres du visage (S30), déterminer l'authenticité de la région d'œil et de la région de lèvres (S40), et déterminer, selon un résultat de détermination, si le visage dans l'image en temps réel est bloqué (S50, S60). Le procédé susmentionné peut déterminer rapidement si le visage dans une image de visage est bloqué.
PCT/CN2017/108751 2017-08-17 2017-10-31 Procédé de détection de situation de visage bloqué, dispositif et support d'informations Ceased WO2019033572A1 (fr)

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