Motion unit layering-based facial expression recognition method and system

Abstract

The invention discloses a motion unit layering-based facial expression recognition method and system. The facial expression recognition method comprises steps of classifying three layers, and specifically comprises the following steps: first extracting an area adjacent to the upper part of the nose as a first-layer classification area, and roughly classifying an expression by taking whether an AU9 motion unit is detected or not as a judgment standard of a first-layer classifier; then extracting a lip area as a second-layer classification area, and performing fine adjustment on the basis of a first-layer classification result by taking whether AU25 and AU12 motion units are detected or not as a judgment standard of a second-layer classifier; finally extracting an upper half face area and a lower half face area as third-layer classification areas respectively, and performing precision classification on the basis of a second-layer classification result. The invention further provides the system for implementing the method. According to the method and the system, characteristics of representative areas of the expression are extracted on the basis of an AU layered structure, and layer-by-layer random forest classification is combined, so that expression recognition accuracy is effectively improved, expression recognition speed is increased, and the method and the system are particularly applied to a low-resolution image.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a method and system for recognizing facial expressions. Background technique [0002] Facial expression recognition is to classify facial expressions by analyzing facial movements and changes in facial features through visual signals. The research on expression classification is basically based on the six main human emotions first proposed by psychologists Ekman and Friesen in 1971. Each emotion reflects a unique psychological activity of a person with a unique expression. These six emotions are called basic emotions and consist of anger, happiness, sadness, surprise, disgust and fear. In recent years, with the rapid development of a series of related fields, such as machine learning, image processing, face detection, etc., more and more attention has been paid to facial expression recognition, and its application prospects are very broad, such as natural human-computer ...

AI Technical Summary

Problems solved by technology

However, the existing methods are generally aimed at high-resolution images and need to locate precise feature points, which are easily affected by noise and have low accuracy.

Images