Multi-sample facial expression recognition method based on low-rank tensor decomposition

Abstract

The invention provides a multi-sample facial expression recognition method based on low-rank tensor decomposition. The multi-sample facial expression recognition method comprises an image preprocessing step, a feature extraction step, a tensor modeling step, a low-rank learning step, a tensor decomposition step and a feature classification step. According to the multi-sample facial expression recognition method, tensor representation feature space is utilized to reserve nonlinear features of an image; and face sub-space area features of different individuals are learned through a low-rank tensor decomposition technology, and face information under different dimensions is obtained, and tensors under all sub-spaces are decomposed, clustered and reconstructed to obtain effective expression features, and the expression information expression capacity is higher, so that the face expression recognition rate is increased.

Description

technical field [0001] The invention relates to the technical field of facial expression recognition, in particular to a method for recognizing diverse facial expressions based on low-rank tensor decomposition. Background technique [0002] In the daily communication of human beings, only 7% of the information transmitted by language is used in human facial expressions, while the information transmitted by human facial expressions is as high as 55%. Research on how computers can understand and express emotions like humans, and can adapt to the environment autonomously, will fundamentally change the connection between humans and computers. Therefore, facial expression recognition has broad application prospects. One is that it can promote the development of multiple subjects such as human-computer interaction, artificial intelligence, psychology, and computer vision, and the other is that it plays a huge role in the application of service industry, reconnaissance and basic di...

AI Technical Summary

Problems solved by technology

That is, the same kind of expression is easily affected by different individuals, and in the research of practical application, the change information of expression features has two characteristics of nonlinearity and continuity, and it is difficult for traditional dimensionality reduction technology to preserve the change of nonlinear characteristics of expression

Images