CN111291670B - Small target facial expression recognition method based on attention mechanism and network integration - Google Patents
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Abstract
The invention relates to a small target facial expression recognition method based on attention mechanism and network integration, which comprises the following steps: aiming at the facial expression data set, data enhancement means including rotation, turning and noise adding are adopted to improve the generalization capability and the training result of the whole network recognition; the network structure comprises a convolution layer and full-connection layer network structure, wherein the network structure comprises two network branches, namely a convolution layer and full-connection layer added with attention and a reduced resnet network; and multiplying the output values of the two networks by corresponding weight parameters respectively, adding the output values with a common bias weight, performing softmax on the obtained result, and finally obtaining a classification result probability value, performing cross entropy calculation on the classification result obtained by the softmax to obtain a loss value in the training process, selecting a value 70% before the loss value, performing reverse propagation on the value to update the weight parameters, wherein an Adam gradient updating method is adopted in an updating strategy.
Description
Technical Field
The invention belongs to the field of classification and identification, and relates to a low-resolution small-target facial expression identification method based on an attention mechanism and network integration (ensemble).
Background
The psychologist Ekman and Friesen research in 1971 suggested that humans have six major emotions, which are called basic emotions, namely: anger (anger), happy (happy), sad (sad), surprise (surrise), disgust (disgust) and fear (fear), each of which expresses a specific psychological activity of a person in a unique expression. On the basis of the emotion recognition, a neutral emotion (normal) is additionally added as a seventh emotion for subsequent classification.
In recent years, as deep learning is greatly developed in an image classification task and an object detection task, a deep learning method is introduced to a human facial expression recognition task, and particularly, the problems of multi-pose, shielding, uneven illumination and the like can be solved by extracting features by using a convolutional neural network. H.Ding, S.K.Zhou [1] proposes a facial expression recognition system Facenet2expnet jointly trained by face recognition and expression recognition, and in the pre-training stage, the expression network of convolution level is trained and regularized through the face network; in the fine tuning stage, an additional full-connection network and a facial expression feature extraction network are trained together to achieve a better facial expression recognition effect. Christopher Pramerdorfer, martin Kampel [2], and the like, in combination with six distinct states of the art deep learning methods, recognize facial expressions. In 2018, sanghyun Woo, jongchan Park [3] proposed a Convolutional Block Attention Module (CBAM), which is a simple and efficient feedforward convolutional neural network attention Module. The module deduces the attention diagrams in turn according to two independent dimensions of channel and space, and then multiplies the attention diagrams into the input feature diagram for adaptive feature refinement under the condition of giving the intermediate feature diagram. Disclosed are a self-adaptive emotion expression system and method based on expression recognition. Patents (CN 201910790582.0) filed by Sun Lingyun, zhou Zihong, et al, zhejiang university all have respective characteristics in terms of network structure.
[1]H.Ding,S.K.Zhou,and R.Chellappa,“Facenet2expnet:Regularizing a deep face recognition net for expression recognition,”in Automatic Face&Gesture Recognition(FG 2017),201712th IEEE International
[2]C.Pramerdorfer andM.Kampel,“Facial expression recognition using convolutional neural networks:State of the art,”arXiv preprint arXiv:1612.02903,2016.
[3]S.Woo,J.Park,J.Lee et al.,CBAM:Convolutional Block Attention Module,in European Conference on Computer Vision(ECCV),2018.
Disclosure of Invention
The invention discloses a small target face expression recognition method based on an attention mechanism and an ensemble. The method can be used for different backgrounds, illumination intensities and weather conditions, and can ensure higher expression recognition accuracy for the long-distance low-resolution small target face. The technical scheme is as follows:
a small target facial expression recognition method based on attention mechanism and network integration comprises the following steps:
firstly, adopting data enhancement means including rotation, turning and noise addition aiming at a facial expression data set to improve the generalization capability and the training result of the whole network identification, and meanwhile, normalizing the picture data and adding expression category labels to obtain 7 expression categories in total.
And secondly, adding a full-connection layer network structure to the convolution layer, wherein the network structure comprises two network branches, namely a convolution plus full-connection layer with an attention mechanism and a reduced renet network, and the method comprises the following steps:
(1) In the convolution layer and the full connection layer, firstly, 64 1*1 convolution cores are used for carrying out preliminary feature acquisition on an image, then feature enhancement is carried out through a channel attention layer and a space attention layer, then a relu activation function and a batch standardization layer are used for enabling a network to have nonlinear features and avoiding gradient disappearance, but a posing layer is not used after the first layer of convolution, so that more data information is reserved; then, the second layer of convolution and the third layer of convolution are both 32 3*3 convolution kernels, and a channel attention and space attention layer, a relu activation function, a batch normalization layer and a maxporoling layer are connected behind the second layer of convolution and the third layer of convolution; the fourth layer of convolution has 64 5*5 convolution kernels, a channel attention and space attention layer, a relu activation function, a batch normalization layer and a maxporoling layer are connected in the same way to obtain a final image characteristic diagram, and finally, the final image characteristic diagram is subjected to three layers of full connection layers to obtain the output value of the convolution added with the attention mechanism and the full connection layers;
(2) In a reduced resnet network, firstly, the width and the height of a characteristic diagram are reduced through 64 convolution kernels of 5*5, then a batch normalization layer and a relu activation function are connected, transmission to a residual network block is started after one time of maxporoling, seven residual network units are used in total, the sizes of the convolution kernels are 3*3, the numbers of the convolution kernels are 32, 64, 128, 256 and 256 respectively, but the step sizes are not 1 any more, but are 1,2,1,2,1,2,1 respectively; obtaining a characteristic diagram through averageposing, and finally obtaining an output value of the reduced renet network through a full connection layer with the output characteristic vector length being the expression category number;
and thirdly, integrating two networks (ensemble), namely multiplying respective output values by corresponding weight parameters and adding the output values, then adding the output values with a common bias weight, and then performing softmax to finally obtain a classification result probability value, performing cross entropy calculation on the classification result obtained by the softmax to obtain a loss value in the training process, and then adopting a top-k loss value training strategy, namely selecting a value 70% of the loss value to perform reverse propagation to update the weight parameters, wherein the updating strategy adopts an Adam gradient updating method.
The invention uses the newly designed convolution network plus the difference of the full connection layer and the reduced resnet network classification to carry out ensemble combination; adding channel attention and spatial attention to the network simultaneously; and in the training process, the loss function value of top-k is adopted for back propagation, so that the over-fitting phenomenon of the network is avoided.
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FIG. 1 is a diagram of an ensemble network structure of convolutional layer plus full link layer and reduced resnet
FIG. 2 confusion matrix results diagram
Detailed Description
In order to make the technical scheme of the invention clearer, the invention is further explained below by combining the attached drawings. The invention is realized by the following steps:
(1) Data preprocessing:
the invention uses the disclosed fer2013 small target low pixel data set, which comprises 28708 training samples, 3589 Zhang Ceshi samples. Each picture is composed of gray images with the fixed size of 48 multiplied by 48, 7 expressions are provided, the expressions correspond to the digital labels 0-6 respectively, and the labels corresponding to the specific expressions are as follows in Chinese and English: 0-anger (gas production); 1-disagust (aversion); 2-fear (fear); 3-happy; 4-sad (heart injury); 5-surprism (surprised); 6-normal (neutral). Carrying out data enhancement and normalization processing on the pixel values of the picture, and converting the pixel values into floating point number types in the range of 0-1
(2) Forward propagation:
the method mainly comprises three parts of convolution plus full connection layer, reduced resnet and ensemble:
firstly, a self-designed convolution layer and full-connection layer network structure is provided, and the specific structure is shown in fig. 1. In the convolution layer and the full connection layer, firstly, 64 1*1 convolution cores are used for carrying out preliminary feature acquisition on an image (the step length in the network structure is set to be 1), then feature enhancement is carried out through a channel attention layer and a space attention layer, then a relu activation function and a batch normalization layer are used for enabling the network to have nonlinear features and avoiding gradient disappearance, but a posing layer is not used after the first layer of convolution, so that more data information is reserved; then, the second layer of convolution and the third layer of convolution are both 32 3*3 convolution kernels, and a channel attention and space attention layer, a relu activation function, a batch normalization layer and a maxporoling layer are connected behind the second layer of convolution and the third layer of convolution; the fourth layer of convolution has 64 5*5 convolution kernels, and the channel attention and space attention layer, the relu activation function, the batch normalization layer and the maxporoling layer are connected in the same way to finally obtain the characteristic diagram of the image. And then, obtaining the final output value of the network through three fully-connected layers, wherein the lengths of the output characteristic vectors of the three fully-connected layers are 2048, 1024 and 7 (the number of categories) respectively. The last full connectivity layer is the result of the classification of the network.
Meanwhile, the input data is also transmitted to the reduced resnet network, and the specific structure is shown in fig. 1. In the reduced resnet network, the width and height of a characteristic diagram are reduced through 64 convolution kernels 5*5, then a batch normalization layer and a relu activation function are connected, transmission to a residual network block is started after one time of maxporoling, seven residual network units are used totally, the sizes of the convolution kernels are 3*3, the numbers of the convolution kernels are 32, 64, 128, 256 and 256 respectively, but the step sizes are not 1 any more, but are 1,2,1,2,1,2,1 respectively. And obtaining a characteristic diagram through averaging, and finally obtaining the network classification result through a full connection layer with the output characteristic vector length of 7.
And finally, respectively multiplying the two network layer ensemble, namely the respective results by the weight parameters, then adding the weighted parameters, adding the common weight to obtain a classification result, and then calculating probability values of all classes by utilizing a softmax function to obtain a final classification result.
(3) Training setting and optimization:
updating the gradient: too small a learning rate can lead to long-time non-convergence and waste of resources; too large a learning rate may result in a local minimum being trapped. The experiment therefore selects the Adam method with an initial learning rate of 10-3 for gradient update.
Loss function: the cross entropy function is used for calculating the loss value, and particularly, the loss function value training strategy of top-k is adopted, namely, the parameters are updated by back propagation 70% of the selected loss value of a sample entering the network, so that the overfitting phenomenon is avoided
(4) Evaluation indexes are as follows:
the accuracy of the test set, the confusion matrix, is shown in FIG. 2.
Claims (1)
1. A small target facial expression recognition method based on attention mechanism and network integration comprises the following steps:
firstly, adopting data enhancement means including rotation, turning and noise addition aiming at a facial expression data set to improve the generalization capability and the training result of the whole network identification, and meanwhile, normalizing picture data and adding expression category labels, wherein the total number of the expression categories is 7;
secondly, a convolution layer and full-connection layer network structure is constructed, the network structure has two network branches which are a convolution plus full-connection layer network and a reduced resnet network with attention mechanisms, and the method comprises the following steps:
(1) Convolution with attention mechanism plus full-link network: in the convolution layer and the full connection layer, firstly, 64 1*1 convolution cores are used for carrying out preliminary feature acquisition on an image, then feature enhancement is carried out through a channel attention layer and a space attention layer, then a relu activation function and a batch standardization layer are used for enabling a network to have nonlinear features and avoiding gradient disappearance, but a posing layer is not used after the first layer of convolution, so that more data information is reserved; then, the second layer of convolution and the third layer of convolution are both 32 3*3 convolution kernels, and a channel attention and space attention layer, a relu activation function, a batch normalization layer and a maxporoling layer are connected behind the second layer of convolution and the third layer of convolution; the fourth layer of convolution has 64 5*5 convolution kernels, a channel attention and space attention layer, a relu activation function, a batch normalization layer and a maxporoling layer are connected in the same way to obtain a final image characteristic diagram, and finally, the final image characteristic diagram is subjected to three layers of full connection layers to obtain the output value of the convolution added with the attention mechanism and the full connection layers;
(2) In a reduced resnet network, firstly, the width and the height of a characteristic diagram are reduced through 64 convolution kernels of 5*5, then a batch normalization layer and a relu activation function are connected, transmission to a residual network block is started after one time of maxporoling, seven residual network units are used in total, the sizes of the convolution kernels are 3*3, the numbers of the convolution kernels are 32, 64, 128, 256 and 256 respectively, but the step sizes are not 1 any more, but are 1,2,1,2,1,2,1 respectively; obtaining a characteristic diagram through averageposing, and finally obtaining an output value of the reduced renet network through a full connection layer with the output characteristic vector length being the expression category number;
thirdly, integrating the convolution plus full-link network and the reduced resnet network (ensemble) added with the attention mechanism, namely multiplying respective output values by corresponding weight parameters and adding the output values, then adding the output values with common partial weights and then performing softmax to finally obtain a classification result probability value, performing cross entropy calculation on the classification result obtained by the softmax to obtain a loss value in the training process, and then adopting a top-k loss value training strategy, namely selecting a value 70% before the loss value to perform reverse propagation to update the weight parameters, wherein the updating strategy adopts an Adam gradient updating method.
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