Facial expression recognition algorithm based on discriminative component analysis

Abstract

The invention relates to a facial expression recognition algorithm based on discriminative component analysis, and particularly relates to a facial expression recognizing method based on an improved DCA (discriminative component analysis). The method comprises the steps of firstly obtaining a sample subset in accordance with principles of a maximum type inner distance and a minimum type inter-distance; secondly calculating a projection matrix of the sample subset; and finally multiplying samples with the projection matrix, and judging the types of the samples through a nearest neighbor method so as to accomplish a facial expression recognition task. With the adoption of the method, not only is the DCA algorithm migrated to the field of facial expression recognition, but also an improved algorithm based on the DCA is provided according to practical conditions; the effectiveness of the algorithm is proved through the experiment result; and 95.71% of average recognition rate is ultimately achieved in a JAFFE (Japan female facial expression) image library.

Description

technical field [0001] The invention relates to a facial expression recognition method based on discriminant component analysis. Background technique [0002] Facial expressions are an important way for human beings to spread emotional information and coordinate interpersonal relationships. Through the recognition and information analysis of human facial expressions, we can obtain the most intuitive psychological and emotional feelings of human beings, and then according to human emotional experience and psychological state, we can be human. Provide technical support for research in the fields of computer interaction and multimedia information processing. In recent years, facial expression recognition technology has received extensive attention from researchers at home and abroad. In 2010, the concept camera that can recognize smiling faces exhibited at the Shanghai World Expo was a wonderful appearance of facial expression recognition technology. [0003] The direct purpo...

AI Technical Summary

Problems solved by technology

[0005] However, the DCA algorithm uses a random selection method to generate sample subsets, which has two disadvantages: on the one hand, random selection cannot guarantee that the selected sample information is conducive to classification, which greatly affects the accuracy of expression recognition; On the other hand, random selection makes the sample subset generated each time different

There is no guarantee that the results obtained by each projection can accurately reflect the distribution of expressions, and because the distance between the sample subsets is too close, a singular matrix will appear during the calculation process, making it impossible to invert the matrix, causing the algorithm to have no solution, and ultimately lead to algorithm performance unstable

Images