Face expression identification method based on video sequences

Abstract

The invention provides a face expression identification method based on video sequences and relates to a method used for identifying graphs. With this method, dynamic space-time texture characteristics of face expression sequences are extracted by use of the HCBP-TOP algorithm. The method comprises steps of preprocessing face expression sequences; performing image layering and partitioning processing for face expression sequences with the space pyramid partition method; extracting dynamic space-time texture characteristics of face expression sequences by use of the HCBP-TOP algorithm; and using an SVM classifier to train and predict face expressions. According to the invention, defects in the prior art are overcome that central pixels are not taken into consideration; local detain information is neglected; identification efficiency and precision of face expressions are quite low; and the traditional method is not widely applicable.

Description

technical field [0001] The technical solution of the present invention relates to a method for recognizing graphics, in particular to a method for recognizing human facial expressions based on video sequences. Background technique [0002] Expression is the most effective way in human emotional communication. In recent years, facial expression recognition systems have important applications in fields involving visual systems and pattern recognition, such as psychological research, video conferencing, emotional computing, and intelligent human-computer interaction. and the medical industry. With the comprehensive improvement of human-computer interaction technology, research on how to make computer systems more capable of perceiving human expressions is the focus of artificial intelligence at present, and the research and development of facial expression recognition systems is of great significance. [0003] Early facial expression recognition methods focused on studying fac...

AI Technical Summary

Problems solved by technology

However, the Gabor feature has the disadvantages of high computational complexity, high dimensionality, and susceptibility to light interference; Wang Yubo from Tsinghua University extracted the Haar-like features of face images in 2003, and then used the algorithm based on continuous Adaboost to classify facial expressions

Haar-like geometric features have some advantages of intuition, low dimensionality and strong description ability, but this method is sensitive to edge features and line features, and the feature extraction accuracy is not high. In addition, when the background environment of the image or video is complex, Adaboost classification device will produce a high false recognition

Liao from the University of North Carolina used the Dominant LBP (DominantLBP, DLBP) and the Gabor method to extract features in 2009, and selected the main feature

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