Human face expression recognition method based on cost sensitive convolutional neural network
A convolutional neural network and facial expression recognition technology, applied in the field of intelligent human-computer interaction, can solve the problems of different costs, reduce the practicability of facial expression recognition models, and greatly differ, and achieve reduced loss and strong facial emotion recognition. ability, the effect of improving the accuracy of facial expression recognition
Active Publication Date: 2018-11-20
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology
In fact, the actual application costs of different expression misclassification results are different or even very different. Ignoring the cost of expression misclassification will inevitably reduce the practicability of the facial expression recognition model
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[0085] The present invention uses the CK+ data set, wherein 80% of the data is used as a training data set for model training, and 20% of the data is used as a test set for model verification. Table 1 shows some experimental data, and the output layer contains 6 types of expressions.
[0086] Table 1 Experimental data
[0087]
[0088] Such as Figure 5 The experimental results shown provide a comparison of the experimental results of the method proposed in the present invention, the existing machine learning method SVM+LBP and the convolutional neural network method. Experimental results show that: using the method proposed in the present invention for facial expression recognition, the overall cost is lower than that of SVM+LBP and CNN methods.
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The invention provides a human face expression recognition method based on a cost sensitive convolutional neural network. On the basis that the loss caused by wrong classification of the human face expression types is considered, the cost caused by wrong classification is fused in the whole process of model training and parameter optimization; in the model training process, different influences caused by wrong classification of different expression types are considered; and model parameters are adaptively adjusted according to the loss caused by wrong classification, so that the loss caused bywrong classification of the expression types is reduced to the maximum extent. The method solves the problem that the influences of the costs caused by wrong classification of different expression types are assumed to be equal or are equal by default in an existing human face expression recognition model based on a convolutional neural network, reduces the loss caused by wrong classification of expressions while improving the expression recognition precision, and has a relatively strong face emotion recognition capability.
Description
technical field [0001] The invention belongs to the technical field of intelligent human-computer interaction, and relates to a facial expression recognition method, specifically, a method for training a facial expression recognition model using a cost-sensitive convolutional neural network and performing facial expression recognition . Background technique [0002] Expression is the expression of human emotion on the face, which contains a lot of human emotion and psychological activity information. Expression recognition aims to mine the hidden emotional characteristics of human faces and perform emotion classification. At present, expression recognition has become a hot spot in the Internet and related industries, especially in the fields of traffic safety, marketing strategy, intelligent human-computer interaction, emotional robot, and smart home. [0003] Facial expression recognition mainly includes three parts: face detection, expression feature extraction, and emoti...
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IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/084G06V40/174G06N3/045
Inventor 李慧芳石峰娟袁艳
Owner BEIJING INSTITUTE OF TECHNOLOGYGY