Facial emotion recognition method based on depth sparse self-encoding network

A sparse self-encoding, facial emotion technology, applied in neural learning methods, character and pattern recognition, biological neural network models, etc., can solve the problems of local optimal gradient, dispersion, etc. Effects of Local Extremum and Gradient Diffusion Problems

CN106503654AInactive Publication Date: 2017-03-15CHINA UNIV OF GEOSCIENCES (WUHAN)
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2017-03-15
Estimated Expiration
Not applicable · inactive patent

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Abstract

The present invention discloses a facial emotion recognition method based on a depth sparse self-encoding network. The method comprises the steps of 1, acquiring and pre-processing data; 2, establishing a depth sparse self-encoding network; 3, automatically encoding / decoding the depth sparse self-encoding network; 4, training a Softmax classifier; and 5, finely adjusting the overall weight of the network. According to the technical scheme of the invention, sparseness parameters are introduced. In this way, the number of neuronal nodes is reduced, and the compressed representation of data can be learned. Meanwhile, the training and recognizing speed is improved effectively. Moreover, the weight of the network is finely adjusted based on the back-propagation algorithm and the gradient descent method, so that the global optimization is realized. The local extremum and gradient diffusion problem during the training process can be overcome, so that the recognition performance is improved.
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Description

technical field

[0001] The invention relates to a face emotion recognition method based on a deep sparse self-encoding network, and belongs to the technical field of pattern recognition. Background technique

[0002] With the rapid development of theories and technologies such as human-computer interaction and affective computing, people generally hope that robots have the ability to recognize, understand and generate human emotions, so as to achieve harmonious, friendly and smooth human-computer communication. Due to the complexity between the diversity of human emotions and the corresponding behaviors, current human-computer interaction still faces some difficult problems in the field of affective computing (including the ability to recognize, understand, and express emotions). Research on emotion recognition based on facial expressions, speech, gestures, physiological signals and other information has become the focus of human-computer interaction. Facial expression reco...

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Embodiment Construction

[0042] The present invention will be further described below in conjunction with drawings and embodiments.

[0043] The present invention provides a face emotion recognition method based on deep sparse self-encoding network, referring to figure 1, by constructing a deep sparse autoencoder network to learn facial expression features and using a Softmax classifier to perform emotion recognition on expressions. First, use the restricted Boltzmann machine to perform layer-by-layer greedy pre-training to obtain the initial weight matrix of the network, expand the model to generate the "encoding" network and "decoding" network, and then build a Softmax classifier on the top of the model and train it. The gradient descent method is used to find the optimal model parameters, and finally the entire network including the Softmax classifier is regarded as a model, and the backpropagation algorithm and the gradient descent method are used to fine-tune the network weights to achieve the gl...