WO2020168731A1 - 一种基于生成对抗机制与注意力机制的标准人脸生成方法 - Google Patents
一种基于生成对抗机制与注意力机制的标准人脸生成方法 Download PDFInfo
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- the invention relates to the technical field of deep learning applications, in particular to a standard face generation method based on a generation confrontation mechanism and an attention mechanism.
- the purpose of the present invention is to solve the above-mentioned shortcomings in the prior art, and provide a standard face generation method based on a generation confrontation mechanism and an attention mechanism, and use a deep learning network framework to design related models to obtain a more standard face.
- the image lays a solid foundation for face recognition in the subsequent single-sample database.
- a standard face generation method based on a generational confrontation mechanism and an attention mechanism includes: data set design steps, model design and training steps, and model prediction steps; the data set design steps are mainly through the current mainstream RaFD data set And IAIR face data set, based on the relevant annotation data of the database, construct a face code with a variety of non-limiting factors for each face image, including facial expression factors, facial posture factors, and shooting light factors.
- the model prediction step is mainly for the predicted result after model processing is performed on the face image acquired in reality.
- the network model includes three sub-networks, which correspond to the image generator sub-network that generates standard faces, the model discriminator sub-network that discriminates the generated results, and the generation
- the resulting restored image is returned to the atomic network; first, the input face image is generated using the image generator sub-network and the attention mechanism; then, the model discriminator sub-network is used to discriminate the generated image, and finally, Construct an image restoration network, restore the generated image, compare the restoration result with the input image, and optimize the network model;
- Model training using the image unit generated in step S1, taking images with multiple non-limiting factors as input to optimize the output and label similarity of the image generator sub-network, model discriminator sub-network, and image reduction atom network , To achieve the convergence of the network model based on the generative confrontation mechanism and the attention mechanism;
- Model prediction extracting the face in the actual image, as the input of the model, by controlling the unified information unit, and finally obtaining a more standard frontal image output.
- step S1 the face information in the face data set is correspondingly encoded, and divided into two types: non-limiting factor face images and standard frontal natural face images;
- step S1 The process of step S1 is as follows;
- a face code with multiple non-limiting factors is constructed for each face image.
- the non-limiting factors include, but are not limited to, facial expression factors, facial posture factors, and shooting lighting Factors etc.
- the facial expression factors are divided into eight situations, which are happy, angry, sad, contemptuous, disappointed, scared, surprised and natural.
- Classification of face data classify the encoded face data into non-limiting factor face images and standard frontal natural clear face images, as follows:
- the corresponding original unified information code is U y
- the generated standard face image is I o
- the unified information code corresponding to the standard face image I o The corresponding standard face image in the database is I
- the unified information code corresponding to the standard face image I is U 0 .
- the input content is image Y and unified information code U 0 .
- the present invention designs two codec networks G c and G f , by combining the attention mechanism to generate the color information mask C and the attention mask F respectively; then, the standard human face is generated through the following synthesis mechanism:
- ⁇ represents the operation of element-wise multiplication of the matrix.
- the codec network G c mainly focuses on the color information and texture information of the human face
- the codec network G f mainly focuses on the areas that need to be changed in the human face
- the input content is the image I o generated by the image generator sub-network.
- the present invention also designs two deep convolutional networks: the image discrimination sub-network D I and the information coding discrimination sub-network D U , which are used to discriminate the generated standard face image I o and the corresponding standard face in the database.
- the difference of the image I, and the unified information coding corresponding to the generated standard face image I o The difference between the unified information code U 0 corresponding to the corresponding standard face image I in the database;
- the input content is the original unified information code U y corresponding to the generated standard face image I o and the input image Y.
- the atomic network is consistent with the image generator sub-network, and the network restoration result is By comparing the restoration result with the input image Y of the overall network, the goal of cyclically optimizing the network result is achieved.
- the generated standard face image I o and the corresponding standard face image I in the database are sent to the image discriminating sub-network D I in the model discriminator sub-network.
- the unified information code corresponding to the standard face image I o will be generated
- the unified information coding U 0 corresponding to the standard face image I in the database is sent to the information coding discrimination sub-network D U in the model discriminator sub-network for discrimination, and through continuous loop optimization, the image generator sub-network and the model discriminator sub-network are made Achieve common progress;
- the present invention designs an image reduction network, and the generated standard face image I o is further restored according to the original unified information code U y corresponding to the original input image Y, and restores The result is compared with the input image Y.
- the entire network achieves the convergence of the overall network model by continuously optimizing the corresponding loss function.
- model training in the step S3 achieves the convergence of the model by optimizing the loss function, wherein the design process of the loss function is specifically as follows:
- conditional expression loss function that is, determine the unified information coding corresponding to the generated standard face image I o and the corresponding standard face image I in the database
- expression of the conditional expression loss function is designed as follows: Where N is the length of the output uniform information encoding. Then, in the conditional expression loss function, adding the mapping relationship between the input image Y and the corresponding original unified information code U y , can improve the discriminative ability of the discriminator.
- conditional expression loss function is designed as U y is the original unified information code corresponding to the input image Y, U 0 is the unified information code corresponding to the standard face image I, and D U (I o ) and D U (Y) are the information coding discriminant sub-network for the image I o Discrimination result with Y;
- step S4 For the generation of the actual face image in step S4, first use the face positioning method based on the face HOG image to obtain the face image in the actual image; then, use the generator trained by the model and the unified information manually set Encoding to realize fast standard face generation of human faces in actual images.
- the generator trained by the model and the unified information manually set Encoding to realize fast standard face generation of human faces in actual images.
- the present invention has the following advantages and effects:
- the present invention applies the deep learning network technology to the standard face generation task to generate color, forward, and standard face images under normal illumination; using the deep learning network method, accurate standard face images can be obtained It reduces the difficulty of matching with the data in the single-sample database, and lays a solid foundation for subsequent facial feature extraction and single-sample face recognition.
- Figure 1 is a flowchart of model training and model application in an embodiment of the present invention
- Figure 2 is a flowchart of data construction of a database in an embodiment of the present invention
- Figure 3 is an overall design diagram of a network model in an embodiment of the present invention.
- Figure 4 is a specific structure diagram of an image generation network in an embodiment of the present invention.
- Fig. 5 is a specific structure diagram of an image discrimination network in an embodiment of the present invention.
- This embodiment discloses a standard face generation method based on a generation confrontation mechanism and an attention mechanism, which mainly involves the following types of technologies: 1) Training data design: Use existing data sets to design unified information coding; 2) Network model structure design: The basic network structure is based on the generation of the confrontation network framework and the loop optimization network method; 3) Standard face generation method: The attention mechanism is added to the generator to restrict the accuracy of standard face generation.
- the TensorFlow framework is a development framework based on the python language, which can quickly and easily build a reasonable deep learning network, and has good cross-platform interaction capabilities.
- TensorFlow provides interfaces for many package functions and various image processing functions in the deep learning architecture, including OpenCV-related image processing functions.
- the TensorFlow framework can also use GPU to train and verify the model, which improves the efficiency of calculation.
- the Pycharm development environment under the Windows platform or Linux platform becomes the development environment (IDE), which is currently one of the first choices for deep learning network design and development.
- IDE development environment
- Pycharm provides customers with new templates, design tools, testing and debugging tools, and can provide customers with an interface to directly call remote servers.
- This embodiment discloses a standard face generation method based on a generation confrontation mechanism and an attention mechanism.
- the main process includes two stages of model training and model application.
- model training stage First, process the existing face data set, and generate a data set that meets the model training by designing a unified information coding mechanism; then, use a cloud server with high computing power to train the network model, and optimize the Loss function, and adjust the network model parameters until the network model converges to obtain the generator structure and weights for generating standard faces.
- model application stage First, use the HOG face image processing method to extract the actual picture to obtain the actual face image; then, call the trained network model to use the face image with non-limiting factors and the designed unified information encoding as Input and generate standard face; finally obtain a colorful, frontal face image.
- Fig. 1 is a flowchart of a standard face generation method based on a generation confrontation mechanism and an attention mechanism disclosed in this embodiment. Specific steps are as follows:
- Step 1 Since the current face database mainly focuses on recognition tasks, there is no face image database with uniform information coding required by the present invention, so it is necessary to integrate the existing databases to construct a suitable database.
- Figure 2 shows the construction process of face image and unified information coding in the database.
- FIG. 3 is a schematic diagram of the overall architecture of the network model.
- the entire model framework mainly includes three sub-networks, which correspond to the image generator sub-network for generating standard faces, the model discriminator sub-network for discriminating the generated results, and the image restoration network for restoring the generated results.
- the image generator sub-network is Parameter sharing is carried out between the network and the image reduction network, and the image generator sub-network mainly combines the attention mechanism to generate face images.
- Fig. 4 is the specific network structure of the image generator sub-network
- Fig. 5 is the specific network structure of the model discriminator sub-network.
- the image generator sub-network has the same parameters as the image-to-atom network, and contains two generators respectively, namely the color information generator and the attention mask generator, as follows:
- the color information generator contains 8 convolutional layers and 7 deconvolutional layers.
- the convolution kernel size of all convolutional layers is 5, the step size is 1, and finally a 3-channel color information image is generated;
- the attention mask generator contains 8 convolutional layers and 7 deconvolutional layers.
- the convolution kernel size of all convolutional layers is 5 and the step length is 1, and finally a 1-channel attention mask is generated.
- the model discriminator sub-network consists of two parts, namely the information encoding discriminating sub-network and the image discriminating sub-network, as follows:
- the information encoding discriminating sub-network includes 6 convolutional layers and 1 fully connected layer, convolutional layer
- the size of the convolution kernel is 5, and the step size is 1, and finally a one-dimensional unified information code of length N is generated;
- the image discrimination sub-network contains 6 convolution layers, the size of the convolution kernel is 5, and the step size is 1.
- Step 3 The training of the model is carried out on a high-performance GPU.
- the specific training parameters are designed as follows: Adam optimizer can be used, and its parameters are set to 0.9/0.999; the learning rate is set to 0.0001; the training epoch is set to 100 ; The batch setting of training depends on the training sample of the data.
- Step 4 model prediction, extract the face in the actual image, as the input of the model, by controlling the unified information unit, and finally obtain a more standard frontal image output.
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Abstract
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Claims (8)
- 一种基于生成对抗机制与注意力机制的标准人脸生成方法,其特征在于,所述的生成方法包括下列步骤:S1、数据构建,采集人脸数据,为每一张人脸图像构建出具有多种非限制因素的人脸编码,然后对人脸数据进行分类,其中,非限制因素包括人脸表情因素、人脸姿态因素和拍摄光照因素,编码后的人脸图像构成信息单元U={L u,E u,A u},包括8位的光照编码L u、8位的表情编码E u以及19位的姿态编码A u;S2、建立基于生成对抗机制与注意力机制的网络模型,该网络模型包括三个子网络,分别对应生成标准人脸的图像生成器子网络、对生成结果进行判别的模型判别器子网络以及通过生成结果进行还原的图像还原子网络;首先,利用图像生成器子网络和注意力机制对输入的人脸图像进行标准脸生成;接着,利用模型判别器子网络,对生成的图像进行判别,最后,构建一个图像还原子网络,对生成图像进行还原,并将还原结果与输入图像进行比较,对网络模型进行优化约束;S3、模型训练,以信息单元U={L u,E u,A u}作为输入,优化图像生成器子网络、模型判别器子网络以及图像还原子网络的输出与标签相似度,实现基于生成对抗机制与注意力机制的网络模型的收敛;S4、模型预测,提取实际图像中的人脸图像,作为网络模型的输入,通过控制信息单元U,最后获得标准的正脸图像输出。
- 根据权利要求1所述的一种基于生成对抗机制与注意力机制的标准人脸生成方法,其特征在于,所述的人脸表情因素分为八种情况,分别为开心、生气、悲伤、轻蔑、失望、害怕、惊讶以及自然,将人脸的表情编码为E u=(E u1,E u2,...,E u8),其 中E ul代表第l种表情,l=0,1,2,…,8,其取值为[0,1],E u=(0,0,...,1)表示为自然表情;所述的人脸光照因素分为八种情况,分别为正面光照、左侧光照、右侧光照、正左光照、正右光照、左右光照、无光照以及全光照,将人脸的光照信息编码为L u=(L u1,L u2,...,L u8),其中L un代表第n种光照情形,其取值为[0,1],L u=(0,0,...,1)表示为全光照图像信息;所述的人脸姿态因素分为19种情况,分别为左侧90°、左侧80°、左侧70°、左侧60°、左侧50°、左侧40°、左侧30°、左侧20°、左侧10°、正脸、右侧10°、右侧20°、右侧30°、右侧40°、右侧50°、右侧60°、右侧70°、右侧80°、右侧90°,将人脸的姿态信息编码为A u=(A u1,A u2,...,A um,...,A u19),其中A um代表第m种人脸姿态,m=0,1,2,…,19,其取值为[0,1],A u=(0,0,...,1)表示为正面姿态信息。
- 根据权利要求2所述的一种基于生成对抗机制与注意力机制的标准人脸生成方法,其特征在于,所述的步骤S1中对人脸数据进行分类过程如下:将编码后的人脸数据分类为非限制因素的人脸图像和标准正面自然清晰人脸图像,其中,将统一编码信息为U 0=(L u(0,0,...,1),E u(0,0,...,1),A u(0,0,...,1),)的人脸图像作为标准正面自然清晰人脸图像,并以此作为模型的目标图像,其余的人脸图像作为非限制因素的人脸图像,并以此作为模型的输入图像。
- 根据权利要求1所述的一种基于生成对抗机制与注意力机制的标准人脸生成方法,其特征在于,所述的图像生成器子网络,其输入是图像Y与标准人脸统一信息编码U 0,图像生成器子网络包括两个编码解码器网络G c和G f,其中,编码解码器网络G c关注人脸的颜色信息与纹理信息,编码解码器网络G f关注人脸中需要变化的区域,通过结合注意力机制,分别生成颜色信息掩码C和 注意力掩码F,接着通过以下的合成机制,生成标准的人脸:C=G c(Y,U 0),F=G f(Y,U 0)I o=(1-F)⊙C+F⊙Y其中⊙表示矩阵的逐元素相乘的操作;所述的模型判别器子网络中,其输入是图像生成器子网络生成的图像I o,模型判别器子网络包括两个深度卷积网络图像判别子网络D I和信息编码判别子网络D U,分别用于判别生成的标准人脸图像I o与数据库中对应的标准人脸图像I的差异,以及生成的标准人脸图像I o所对应的统一信息编码 与数据库中对应的标准人脸图像I所对应的统一信息编码U 0之间的差异;
- 根据权利要求4所述的一种基于生成对抗机制与注意力机制的标准人脸生成方法,其特征在于,所述的步骤S2过程如下:首先,将输入图像Y和标准人脸图像I对应的统一信息编码U 0输入到融合注意力机制的图像生成器子网络中,用于生成标准人脸图像I o;接着,将生成的标准人脸图像I o和数据库中对应的标准人脸图像I送入模型判别器子网络中深度卷积网络D I进行判别,同时,将生成标准人脸图像I o对应的统一信息编码 与数据库中标准人脸图像I对应的统一信息编码U 0送入模型判别器子网络中深度卷积网络D U进行判别,使得图像生成器子网络和模型判别器子网络同时实现优化;
- 根据权利要求1所述的一种基于生成对抗机制与注意力机制的标准人脸生成方法,其特征在于,所述的步骤S3中模型训练通过优化损失函数,实现模型的收敛,其中,所述的损失函数设计过程如下:优化判别生成的标准人脸图像I o与数据库中对应的标准人脸图像I之间的差异:设置图像损失函数如下所示 其中H与W分别是输出的人脸图像的高度与宽度,D I(I o)与D I(I)分别为图像判别子网络对图像I o与I的评判结果;然后,考虑到梯度损失的有效性,在图像损失函数中加上基于梯度的惩罚项,即图像损失函数设计为 其中 表示图像的梯度操作,λ I为惩罚项权重;优化图像还原子网络的结果与原始输入图像之间的差异:通过输入生成器所生成的图像I o与原始统一信息编码U y进行还原,进而与原始输入图 像Y进行比较,因此,还原损失函数设计为 其中h与w表示图像的的高度与宽度,G代表图像生成器子网络;整个网络模型的损失函数如下:L=L I+L U+L r。
- 根据权利要求1所述的一种基于生成对抗机制与注意力机制的标准人脸生成方法,其特征在于,所述的步骤S4过程如下:首先,使用基于人脸HOG图像的人脸定位方法,获取实际图像中的人脸图像;然后,利用网络模型训练的生成器以及人工设置的统一信息编码,实现对实际图像中人脸的快速标准脸生成。
- 根据权利要求1所述的一种基于生成对抗机制与注意力机制的标准人脸生成方法,其特征在于,所述的步骤S1中采集RaFD人脸数据集和IAIR人脸数据集中人脸数据。
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