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

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.

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...

AI Technical Summary

Problems solved by technology

However, some existing machine learning algorithms are prone to problems such as local optima and gradient dispersion in the process of training and recognizing facial expression features. Therefore, more discriminative facial feature selection and classifiers with good classification capabilities Design is the key to improving facial emotion recognition rate

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