Large-scale facial expression recognition method based on multiscale LBP and sparse coding

Abstract

The invention provides a large-scale facial expression recognition method based on a multiscale LBP and sparse coding. The method comprises the steps that a large-scale facial expression database is built firstly, a training database and a testing database are generated based on a random sampling technology, then facial expression features are expressed through multiscale LBP features, then a dictionary needed in a sparse coding method is generated, a new expression sample is solved to obtain an optimal sparse coefficient, and the sparse coefficients of different expressions are accumulated to recognize the expression samples. According to the method, a high-robustness feature expressing mode is obtained through the multiscale LBP features, the sparse problem in large-scale facial expression recognition is solved through sparse coding, and the effectiveness of the large-scale facial expression recognition method based on the multiscale LBP and sparse coding is verified.

Description

technical field [0001] The invention relates to a face recognition method, in particular to a face recognition method. Background technique [0002] Facial expression recognition is an important research direction in the fields of pattern recognition, human-computer interaction and biometric recognition, and has become a hot research topic at home and abroad. The six most common basic human expressions are happiness, sadness, anger, surprise, nausea and fear. There are not only large changes in the shape of the human face, but also subtle changes among the six expressions. Therefore, expression recognition needs to ignore the changes of different faces and focus on recognizing the differences between facial expressions. Document 1 "X. Wang, X Feng, and J Peng. A Novel Facial Expression Database Construction Method based on Web Images. In Proc. of the Third Intl. Conf. on Internet Multimedia Computing and Service (ICIMCS'11). 2011, pp124- 127” proposed a method for buildin...

AI Technical Summary

Problems solved by technology

This method constructs a large-scale database of natural facial expressions and increases the capacity of expression samples. However, the face changes in the database are relatively large. Traditional expression features are sensitive to face changes and are easily blocked by faces. At the same time, it is difficult for traditional expression recognition methods such as k-nearest neighbors to find relatively similar expressions in training samples, and large-scale expression samples are relatively sparse, which affects the accuracy of recognition.

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