Expression identification method fusing depth image and multi-channel features

Abstract

The invention discloses an expression identification method fusing a depth image and multi-channel features. The method comprises the steps of performing human face region identification on an input human face expression image and performing preprocessing operation; selecting the multi-channel features of the image, extracting a depth image entropy, a grayscale image entropy and a color image salient feature as human face expression texture information in the texture feature aspect, extracting texture features of the texture information by adopting a grayscale histogram method, and extracting facial expression feature points as geometric features from a color information image by utilizing an active appearance model in the geometric feature aspect; and fusing the texture features and the geometric features, selecting different kernel functions for different features to perform kernel function fusion, and transmitting a fusion result to a multi-class support vector machine classifier for performing expression classification. Compared with the prior art, the method has the advantages that the influence of factors such as different illumination, different head poses, complex backgrounds and the like in expression identification can be effectively overcome, the expression identification rate is increased, and the method has good real-time property and robustness.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to image processing, human-computer interaction, and in particular to facial expression recognition technology. Background technique [0002] Facial expression interaction is an important research content of human-computer interaction and affective computing. Facial expressions are the most eloquent and equally important way for humans to communicate emotions, express intentions and even regulate natural interactions with other humans. Facial expressions can often convey many things that words cannot. Facial expressions can be divided into macro-expressions and micro-expressions. Macro-expressions are facial signals that people show in a normal state; while micro-expressions are short-lived, latent expressions that are usually hidden or suppressed intentionally or unintentionally. their inner emotions. Facial movements reflect not only facial emotions, but also other kin...

AI Technical Summary

Problems solved by technology

However, it mainly uses geometric features for classification, does not consider texture information, and lacks optimization for multi-classification support vectors.

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