WO2021051543A1 - Procédé de génération de modèle de rotation de visage, appareil, dispositif informatique et support de stockage - Google Patents

Procédé de génération de modèle de rotation de visage, appareil, dispositif informatique et support de stockage Download PDF

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WO2021051543A1
WO2021051543A1 PCT/CN2019/117299 CN2019117299W WO2021051543A1 WO 2021051543 A1 WO2021051543 A1 WO 2021051543A1 CN 2019117299 W CN2019117299 W CN 2019117299W WO 2021051543 A1 WO2021051543 A1 WO 2021051543A1
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face
model
parameters
dimensional
rotation
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PCT/CN2019/117299
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English (en)
Chinese (zh)
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田笑
陈嘉莉
周超勇
刘玉宇
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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
    • G06V40/168—Feature extraction; Face representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T19/00—Manipulating 3D models or images for computer graphics
    • G06T19/20—Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/60—Rotation of whole images or parts thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/64—Three-dimensional objects
    • 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
    • G06V40/172—Classification, e.g. identification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00—Indexing scheme for image data processing or generation, in general
    • G06T2200/04—Indexing scheme for image data processing or generation, in general involving 3D image data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30196—Human being; Person
    • G06T2207/30201—Face
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2219/00—Indexing scheme for manipulating 3D models or images for computer graphics
    • G06T2219/20—Indexing scheme for editing of 3D models
    • G06T2219/2016—Rotation, translation, scaling

Definitions

  • This application relates to the field of model construction, and in particular to a method, device, computer equipment and storage medium for generating a face rotation model.
  • the face recognition technology in the field of artificial intelligence is not mature enough, and the face recognition technology for non-frontal faces has always been a problem in the field of face recognition.
  • the face alignment technology of non-frontal faces in the existing face recognition system can usually only solve the rotation of the roll angle in the face pose, and the similar transformation is directly used for the large-angle yaw angle or the pitch angle. In this way, The face will be deformed greatly after the alignment, which is not conducive to subsequent face recognition; at the same time, the face alignment of a non-frontal face in the prior art cannot obtain accurate three-dimensional coordinate information of the face. Therefore, finding a technical solution that can solve the above-mentioned problems has become an urgent problem for those skilled in the art.
  • a method for generating a face rotation model including:
  • the real position map includes the face image in the face training picture The corresponding front face and the three-dimensional position information of all first key points of the front face;
  • Extract the target feature of the face image in the face training picture input the target feature into a face rotation model containing initial parameters, and obtain all second key points output by the face rotation model 3D location information;
  • the three-dimensional position information of each of the first key points and the weight value of the three-dimensional position information of each of the second key points in the mean square error loss function are set through a mask, and the value of each of the first key points is The three-dimensional position information and the three-dimensional position information of each of the second key points are input into the mean square error loss function to obtain a loss result;
  • the face rotation model training is completed; the face rotation model is used to confirm the face when the face photo is input into the face rotation model
  • the photo rotation is the required rotation angle for the front face photo.
  • a device for generating a face rotation model including:
  • the first acquisition module is configured to acquire face training pictures from the target set; the face training pictures have already marked three-dimensional face parameters;
  • the second acquiring module is configured to acquire a preset average face model, and acquire a real position map according to the three-dimensional face parameters and the preset average face model; the real position map contains the same face as the face model.
  • the third acquisition module is configured to extract the target feature of the face image in the face training picture, input the target feature into a face rotation model containing initial parameters, and obtain the face rotation model The three-dimensional position information of all the output second key points;
  • the input module is used to set the three-dimensional position information of each of the first key points and the weight value of the three-dimensional position information of each of the second key points in the mean square error loss function through a mask, and set each of the The three-dimensional position information of the first key point and the three-dimensional position information of each of the second key points are input into the mean square error loss function to obtain a loss result;
  • the first confirmation module is used to confirm that the face rotation model training is completed when the loss result is less than or equal to the preset loss value; the face rotation model is used to input the face photo into the face rotation model , Confirm that the face photo is rotated to the required rotation angle for the front face photo.
  • a computer device includes a memory, a processor, and computer-readable instructions that are stored in the memory and can run on the processor, and the processor implements the following steps when the processor executes the computer-readable instructions:
  • the real position map includes the face image in the face training picture The corresponding front face and the three-dimensional position information of all first key points of the front face;
  • Extract the target feature of the face image in the face training picture input the target feature into a face rotation model containing initial parameters, and obtain all second key points output by the face rotation model 3D location information;
  • the three-dimensional position information of each of the first key points and the weight value of the three-dimensional position information of each of the second key points in the mean square error loss function are set through a mask, and the value of each of the first key points is The three-dimensional position information and the three-dimensional position information of each of the second key points are input into the mean square error loss function to obtain a loss result;
  • the face rotation model training is completed; the face rotation model is used to confirm the face when the face photo is input into the face rotation model
  • the photo rotation is the required rotation angle for the front face photo.
  • One or more readable storage media storing computer readable instructions, when the computer readable instructions are executed by one or more processors, the one or more processors execute the following steps:
  • the real position map includes the face image in the face training picture The corresponding front face and the three-dimensional position information of all first key points of the front face;
  • Extract the target feature of the face image in the face training picture input the target feature into a face rotation model containing initial parameters, and obtain all second key points output by the face rotation model 3D location information;
  • the three-dimensional position information of each of the first key points and the weight value of the three-dimensional position information of each of the second key points in the mean square error loss function are set through a mask, and the value of each of the first key points is The three-dimensional position information and the three-dimensional position information of each of the second key points are input into the mean square error loss function to obtain a loss result;
  • the face rotation model training is completed; the face rotation model is used to confirm the face when the face photo is input into the face rotation model
  • the photo rotation is the required rotation angle for the front face photo.
  • FIG. 1 is a schematic diagram of an application environment of a method for generating a face rotation model in an embodiment of the present application
  • FIG. 2 is a schematic flowchart of a method for generating a face rotation model in an embodiment of the present application
  • step S20 of the method for generating a face rotation model in an embodiment of the present application
  • FIG. 4 is a schematic structural diagram of an apparatus for generating a face rotation model in an embodiment of the present application
  • Fig. 5 is a schematic diagram of a computer device in an embodiment of the present application.
  • the method for generating a face rotation model provided in this application can be applied in an application environment as shown in FIG. 1, where the client communicates with the server through the network.
  • the client can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.
  • the server can be implemented as an independent server or a server cluster composed of multiple servers.
  • a method for generating a face rotation model is provided. Taking the method applied to the server in FIG. 1 as an example for description, the method includes the following steps:
  • S10 Obtain a face training picture from a target set; the face training picture has been marked with three-dimensional face parameters;
  • the target set can be a large public face data set (such as the 300W-LP public set, which contains more than 60,000 human pictures, and each face training picture has annotated three-dimensional face parameters.
  • Face parameters include face shape parameters, facial expression parameters, and face pose parameters).
  • the face photos in the face data set can be used as face training photos; face training images include face rotation
  • face training images include face rotation
  • the 43867 face vertices required in the model modeling process the three-dimensional coordinate information corresponding to the face vertices has not been determined yet).
  • the classic model Basel Face Model in the successfully trained and open source 3DMM model includes an average face model, and the average face model can represent a standard average face (and the average face model data comes from :Facial features extracted from a preset number of ordinary faces. According to the measurement of facial features, each measurement data is obtained. Finally, each average value is calculated based on each measurement data, that is, each average value can be used as the data of the composite average face model ); The three-dimensional face parameters can be compared with the model parameters in the average face model; the real position map is a standard frontal face picture.
  • the real position map is the same size as the face training picture, and both can be 256x256x3( 256x256 represents pixels in the horizontal and vertical directions respectively), this real position map will be used as the learning target in this training process; the three-dimensional position information refers to the three-dimensional coordinates in the three-dimensional coordinate system.
  • the real position map obtained in this embodiment can be used as a learning target in the training process of the face rotation model.
  • the initial parameters of the face rotation model can be adjusted in the subsequent training process, so that a computable face photo rotation can be obtained.
  • the face rotation model of the rotation angle required for the front face photo can be used as a learning target in the training process of the face rotation model.
  • the three-dimensional face parameters include face shape parameters, facial expression parameters, and face pose parameters; the obtaining a preset average face model is based on the three-dimensional face parameters and The preset average face model to obtain the real position map includes:
  • the model parameters include model shape parameters and model expression parameters;
  • S202 Determine the deformation of the face image in the face training picture according to the face shape parameter and the model shape parameter;
  • S203 Determine the expression change of the face image in the face training picture according to the face expression parameter and the model expression parameter;
  • S204 Determine, according to the average face shape, the deformation of the face image, and the expression change of the face image, that the face image corresponds to each of the faces in the preset average face model Three-dimensional position information of vertices;
  • the face training pictures are similar Transform to the frontal position to obtain the real position map of the frontal face at the frontal position corresponding to the face image in the face training picture.
  • the preset model parameters of the average face model include model shape parameters and model expression parameters.
  • the model shape parameters and model expression parameters are used to reflect the appearance and state of the corresponding average face in the average face model. , That is, what the shape of the average face of most people should look like (the outline of the entire face and the size of the face are the standard average face), and what should the expression of the average face of most people look like (face keeping Slight smile or non-smiling face, etc.);
  • the preset average face model corresponds to an average face shape, and the average face shape is an overall face shape (including the shape of various facial sense organs, such as mouth, eyes and nose, etc.) ,
  • Each vertex on the average face shape can be represented by three-dimensional position information, and the average face shape is first composed of each face vertex and line (the line between the face vertex and the face vertex) to form each two-dimensional Plane, and then assembled by each two-dimensional plane.
  • the preset model parameters of the average face model can be used to determine the difference between the three-dimensional face parameters in the face training picture and the standard parameters. Firstly, by comparing the face shape parameters with the model shape parameters The difference determines the deformation of the face image in the face training picture (the face shape parameters in the face training picture can include multiple, such as the national character face and the melon face), by comparing the facial expression parameters and the model expression The parameters determine the expression changes of the face image (the facial expression parameters in the face training images can include multiple, such as angry and happy, etc. The expression changes will affect the shape of the various sense organs in the entire face).
  • the average face shape and the deformation of the face image and the expression change of the face image can be used to determine the face image in the face training relative to the face in the preset average face model.
  • the three-dimensional position information of the vertex for example, the average face shape corresponds to a non-smiling facial expression, that is, the corners of the mouth will not rise or stretch out. If the face image in the face training image corresponds to a smiling expression at this time, that is The corners of the mouth have been raised or stretched.
  • the training picture can be determined at this time The face vertices where the face image has been raised or stretched, so that the three-dimensional position information can be determined.
  • each facial sensory in the face image corresponds to the face vertices in the preset average face model The three-dimensional location information.
  • the three-dimensional position information determines the real position map of the frontal face corresponding to the face image in the face training picture at the frontal position, that is, by similarly transforming the face training picture to the frontal position, for example, as mentioned in the above example
  • the face image of the face training picture is not in the front position (that is, there is a certain deviation from the front position).
  • similar transformation can be used to determine the three-dimensional position information of the transformed first key point (located on the real position map). ), the real position map of the front position can also be obtained.
  • the real position map obtained by the above method steps has accurate three-dimensional position information of the first key point, which provides an accurate reference for the subsequent steps to accurately calculate the rotation angle.
  • the face training image contains the data to be input to the face rotation model that contains the initial parameters.
  • This data can be extracted and used as the target feature of the face image (the target feature still exists in the form of a picture)
  • the target feature can be the key points needed to construct the various sensory areas of the face (such as constructing the key points of the eye area, constructing the key points of the nose area, and constructing the key points of the mouth area, etc.);
  • the face rotation model is The classic model in the 3DMM model, Basel Face Model.
  • the input target feature is input in the form of a picture (the same size as the face training picture) into the overall network structure (where the target feature is)
  • the pictures in the overall structure will become feature maps of different sizes between the base layers of the transposed rolls), there are 10 residual modules in the overall network structure, that is, the depth of the overall network structure can be deepened and the overall network structure can be deepened by the residual module first.
  • the output is a set of heatmaps, that is, the three-dimensional position information of all the second key points in the result location map), and there is also a loss layer (used to set the loss function) in the overall network structure.
  • the function of feature extraction is to initially obtain data that is more convenient for the operation and calculation of the face rotation model containing the initial parameters, thereby reducing the complexity of the overall network structure operation process in the face rotation model, and also reducing The running time of the running process; and the face rotation model can be further used to capture the deep information contained in the picture where the target feature is located in different picture sizes.
  • the method further includes:
  • the face rotation model extract the three-dimensional position information of all the second key points from the result position map according to a preset key point index.
  • the resulting position map is a picture of the same size as the real position map and the face training picture;
  • the preset key points can be the key points in the front face, including the eyes, nose and mouth in the sensory area There are multiple key points in a sensory area.
  • the extracted three-dimensional position information of the second key point will be used for comparison with the three-dimensional position information of the second key point, so that the rotation angle of the face rotation model can be calculated.
  • the step of making the face rotation model to extract the three-dimensional position information of all the second key points from the result position map according to a preset key point index includes:
  • Make the face rotation model determine the key points corresponding to the fixed sensory area in the result location map according to the preset key point index, and extract the three-dimensional coordinates of each of the key points from the three-dimensional coordinate system And record the three-dimensional coordinate information of each of the key points as the three-dimensional position information of each of the second key points.
  • the result location map is located in a three-dimensional coordinate system, that is, each fixed sensory area (the area composed of the eyes, nose, and mouth) in the result location map. At this time, it is indexed from multiple fixed sensory areas.
  • the preset key points determine the required key points, and extract the three-dimensional coordinate information of the key points, and the three-dimensional coordinate information of the last key point can be used as the three-dimensional position information of the second key point.
  • the three-dimensional position information of each first key point and the amount of the three-dimensional position information of each second key point in the mean square error loss function can be set in the numerical part of the mean square error loss function through the mask.
  • Weight value that is, set the weight value of the first key point and the second key point in the mean square error loss function, and the set first key point and second key point should be the key points corresponding to the same sensory area (For example, the first key point is the area where the eyes are located, that is, the second key point is also the area where the eyes are located), and the sensory area includes areas where the eyes, nose, and mouth are located (and there are a total of 68 key points in these three areas Composition, that is, the first key point must be greater than or equal to 68, and the second key point must be greater than or equal to 68) and other face areas. At this time, the areas where the eyes, nose, mouth and other face areas are in the mean square The ratio of the weight value in the error loss function is set to 16:4:3:0.
  • n is the number of key or the second key point of the first point
  • y i is the i-th vector set of three-dimensional position information of the second key is constituted
  • y 'i is the i th first key A set of vectors composed of three-dimensional position information.
  • the masks are used to set each The weight value of the three-dimensional position information of a key point and the three-dimensional position information of each second key point in the mean square error loss function is helpful for the face rotation model to learn a more accurate real position map.
  • the loss result is less than or equal to the preset loss value, it can indicate that the face rotation model regression training process is close to the training target. At this time, it can be determined that the face rotation model training is completed.
  • step S40 it further includes:
  • the loss result is greater than the preset loss value, it can indicate that the initial parameters of the face rotation model are not suitable for this training process. Therefore, it is necessary to continuously update the initial parameters of the face rotation model until the condition is suitable for This training process.
  • step S50 it further includes:
  • Input a face photo into the trained face rotation model obtain the rotation angle output by the face rotation model, and rotate the face photo into the frontal photo according to the rotation angle;
  • the rotation angle refers to a rotation angle required to rotate the face photo into a front face photo.
  • this embodiment is an application of the face rotation model.
  • the rotation angle needs to be rotated (the face rotation model can be used in the original training process).
  • the face rotation model can be used in the original training process.
  • the face photo and the front photo can accept a certain angle deviation (for example, within 0.5 degrees, etc.) within the preset deformation range.
  • the rotating the face photo into the front face photo according to the rotation angle includes: extracting the target feature of the face photo, and inputting the target feature of the face photo to In the trained face rotation model, and obtain the three-dimensional position information of all third key points output by the trained face rotation model; according to the rotation angle and the three-dimensional position information of the third key point Rotate the face photo into the front face photo.
  • the foregoing provides a method for generating a face rotation model, which obtains face training images from a target set; the face training images have been marked with three-dimensional face parameters; and a preset average face model is obtained , Obtaining a real position map according to the three-dimensional face parameters and the preset average face model; the real position map includes the frontal face corresponding to the face image in the face training picture and the The three-dimensional position information of all the first key points of the frontal face; extract the target feature of the face image in the face training picture, input the target feature into the face rotation model containing initial parameters, and Obtain the three-dimensional position information of all the second key points output by the face rotation model; set the three-dimensional position information of each of the first key points and the three-dimensional position information of each of the second key points in the mean square error The weight value occupied in the loss function, and input the three-dimensional position information of each of the first key points and the three-dimensional position information of each of the second key points into the mean square error loss function to obtain a
  • a device for generating a face rotation model in one embodiment, corresponds to the method for generating a face rotation model in the above-mentioned embodiment in a one-to-one correspondence.
  • the device for generating the face rotation model includes a first acquisition module 11, a second acquisition module 12, a third acquisition module 13, an input module 14 and a first confirmation module 15.
  • the detailed description of each functional module is as follows:
  • the first obtaining module 11 is configured to obtain face training pictures from a target set; the face training pictures have already marked three-dimensional face parameters;
  • the second acquiring module 12 is configured to acquire a preset average face model, and acquire a real position map according to the three-dimensional face parameters and the preset average face model; the real position map contains the same as the person The three-dimensional position information of the front face corresponding to the face image in the face training picture and all first key points of the front face;
  • the third acquisition module 13 is configured to extract the target feature of the face image in the face training picture, input the target feature into a face rotation model containing initial parameters, and obtain the face rotation 3D position information of all second key points output by the model;
  • the input module 14 is configured to set the three-dimensional position information of each of the first key points and the weight value of the three-dimensional position information of each of the second key points in the mean square error loss function through a mask, and set Inputting the three-dimensional position information of the first key point and the three-dimensional position information of each of the second key points into the mean square error loss function to obtain a loss result;
  • the first confirmation module 15 is used to confirm that the face rotation model training is completed when the loss result is less than or equal to the preset loss value; the face rotation model is used to input a face photo into the face rotation When modeling, confirm that the face photo is rotated to the required rotation angle for the front face photo.
  • the second acquisition module includes:
  • the first acquisition sub-module is used to acquire the model parameters of the preset average face model and the average face shape that has been successfully trained;
  • the average face shape refers to the three-dimensional position of a preset number of face vertices
  • the model parameters include model shape parameters and model expression parameters;
  • the first determining sub-module is configured to determine the deformation of the face image in the face training picture according to the face shape parameter and the model shape parameter;
  • the second determining submodule is configured to determine the facial expression change of the facial image in the facial training picture according to the facial facial expression parameters and the model facial expression parameters;
  • the third determining sub-module is configured to determine that the face image corresponds to the preset average face model according to the average face shape, the deformation of the face image, and the expression change of the face image
  • the transformation sub-module is configured to convert the person according to the face pose parameters and the determined three-dimensional position information of the face image corresponding to each of the face vertices in the preset average face model
  • the face training picture is similarly transformed into the front face position, and the real position map of the front face corresponding to the face image in the face training picture at the front face position is obtained.
  • the device for generating the face rotation model further includes:
  • a fourth acquisition module configured to enable the face rotation model to acquire a result position map equal in size to the real position map according to the target feature
  • the extraction module is configured to enable the face rotation model to extract the three-dimensional position information of all the second key points from the result position map according to a preset key point index.
  • the extraction module includes:
  • the establishment sub-module is used to enable the face rotation model to establish a three-dimensional coordinate system in the result position map
  • the fourth determining sub-module is used to make the face rotation model determine the key points corresponding to the fixed sensory area in the result position map according to the preset key point index, and extract them from the three-dimensional coordinate system
  • the three-dimensional coordinate information of each key point, and the three-dimensional coordinate information of each key point is recorded as the three-dimensional position information of each second key point.
  • the device for generating the face rotation model further includes:
  • the rotation module is used to input a face photo into the face rotation model that has been trained, obtain the rotation angle output by the face rotation model, and rotate the face photo according to the rotation angle.
  • the rotation module includes:
  • the second acquisition sub-module is used to extract the target feature of the face photo, input the target feature of the face photo into the face rotation model after training, and obtain the face rotation after training 3D position information of all third key points output by the model;
  • the rotation sub-module is configured to rotate the face photo into the front face photo according to the rotation angle and the three-dimensional position information of the third key point.
  • the device for generating the face rotation model further includes:
  • the second confirmation module is configured to iteratively update the initial parameters of the face rotation model when the loss result is greater than the preset loss value, until the loss result is less than or equal to the preset loss value To confirm that the training of the face rotation model is completed.
  • Each module in the device for generating a face rotation model described above can be implemented in whole or in part by software, hardware, and a combination thereof.
  • the above-mentioned modules may be embedded in the form of hardware or independent of the processor in the computer equipment, or may be stored in the memory of the computer equipment in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.
  • a computer device is provided.
  • the computer device may be a server, and its internal structure diagram may be as shown in FIG. 5.
  • the computer equipment includes a processor, a memory, a network interface, and a database connected through a system bus.
  • the processor of the computer device is used to provide calculation and control capabilities.
  • the memory of the computer device includes a readable storage medium and an internal memory.
  • the readable storage medium stores an operating system, computer readable instructions, and a database.
  • the internal memory provides an environment for the operation of the operating system and computer readable instructions in the readable storage medium.
  • the database of the computer device is used to store the data involved in the generation method of the face rotation model.
  • the network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instructions are executed by the processor, a method for generating a face rotation model is realized.
  • the readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.
  • a computer device including a memory, a processor, and computer-readable instructions stored in the memory and capable of running on the processor, and the processor implements the following steps when the processor executes the computer-readable instructions:
  • the real position map includes the face image in the face training picture The corresponding front face and the three-dimensional position information of all first key points of the front face;
  • Extract the target feature of the face image in the face training picture input the target feature into a face rotation model containing initial parameters, and obtain all second key points output by the face rotation model 3D location information;
  • the three-dimensional position information of each of the first key points and the weight value of the three-dimensional position information of each of the second key points in the mean square error loss function are set through a mask, and the value of each of the first key points is The three-dimensional position information and the three-dimensional position information of each of the second key points are input into the mean square error loss function to obtain a loss result;
  • the face rotation model training is completed; the face rotation model is used to confirm the face when the face photo is input into the face rotation model
  • the photo rotation is the required rotation angle for the front face photo.
  • one or more readable storage media storing computer readable instructions are provided.
  • the readable storage media provided in this embodiment include non-volatile readable storage media and volatile readable storage.
  • the real position map includes the face image in the face training picture The corresponding front face and the three-dimensional position information of all first key points of the front face;
  • Extract the target feature of the face image in the face training picture input the target feature into a face rotation model containing initial parameters, and obtain all second key points output by the face rotation model 3D location information;
  • the three-dimensional position information of each of the first key points and the weight value of the three-dimensional position information of each of the second key points in the mean square error loss function are set through a mask, and the value of each of the first key points
  • the three-dimensional position information and the three-dimensional position information of each of the second key points are input into the mean square error loss function to obtain a loss result;
  • the face rotation model training is completed; the face rotation model is used to confirm the face when the face photo is input into the face rotation model
  • the photo rotation is the required rotation angle for the front face photo.
  • a person of ordinary skill in the art can understand that all or part of the processes in the methods of the above-mentioned embodiments can be implemented by instructing relevant hardware through computer-readable instructions.
  • the computer-readable instructions can be stored in a non-volatile computer.
  • a readable storage medium or a volatile readable storage medium when the computer readable instruction is executed, it may include the processes of the above-mentioned method embodiments.
  • any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and/or volatile memory.
  • Non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Channel (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

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Abstract

La présente invention concerne un procédé de génération d'un modèle de rotation de visage, un appareil, un dispositif informatique et un support de stockage. Le procédé consiste à : acquérir une image d'apprentissage de visage ; acquérir une carte de position réelle en fonction de paramètres de visage en trois dimensions et d'un modèle de visage moyen prédéfini ; extraire des caractéristiques cibles dans l'image d'apprentissage de visage, entrer les caractéristiques cibles dans un modèle de rotation de visage et acquérir des informations de position en trois dimensions de tous les seconds points clés fournis par le modèle de rotation de visage ; utiliser un masque pour configurer une valeur de pondération occupée par des informations de position en trois dimensions de chaque premier point clé et des informations de position en trois dimensions de chaque second point clé dans une fonction de perte d'erreur moyenne, et entrer les informations de position en trois dimensions de chaque premier point clé et les informations de position en trois dimensions de chaque second point clé dans la fonction de perte d'erreur moyenne pour obtenir un résultat de perte ; et lorsque le résultat de la perte est inférieur ou égal à une valeur de perte prédéfinie, confirmer qu'un modèle de rotation de visage est complètement entraîné. En utilisant le procédé décrit, l'angle de rotation requis pour faire tourner une photographie de visage afin d'obtenir une photographie de visage de face peut être calculé avec précision.
PCT/CN2019/117299 2019-09-18 2019-11-12 Procédé de génération de modèle de rotation de visage, appareil, dispositif informatique et support de stockage WO2021051543A1 (fr)

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