Method of detection, division, and expression recognition of human face based on layered TDP model

Abstract

The invention discloses a method of detection, division, and expression recognition of the natural face based on a layered TDP model. Firstly, an original image is subjected to pre-treatment; an SIFT characteristic and corresponding position information are extracted; the two characteristics are combined by adopting the effective transference Dirichlet process to obtain a characteristic vector with geometric constraint; and the characteristic vector is input to the TDP model to obtain a first layer result-a human face or a non-human face. Then, the human face after division is taken as input of a second layer, and a division result of a subarea is obtained through the same process. Finally, the subarea is taken as the input of a third layer that is an expression recognition layer of the human face, and a result of expression recognition of a human face image is obtained by the same processes of characteristic extraction and combination. According to the invention, the problems that a model is needed to be established for every gesture in conventional multi-gesture expression recognition, and the model recognition rate is low due to factors such as a gesture are solved, and accuracy of expression recognition of a multi-gesture human face image is effectively improved.

Description

technical field [0001] The invention belongs to the field of emotion recognition, and in particular relates to a method for face detection, segmentation and expression recognition in a natural environment based on a hierarchical TDP model. Background technique [0002] Psychologist J.A. Russell proposed that in people's daily communication, only 7% of the information is transmitted through language, while 55% of the information is transmitted through facial expressions. It can be seen that facial expression is a very important way of information transmission. It is a rich source of information for human behavior and can convey information that many languages ​​cannot convey. In recent years, with the continuous improvement of some applications, the development of facial expression recognition technology has been promoted. [0003] In A.S.Willsky et al., entitled "Describing Visual Scenes Using Transformed Objects and Parts", the transfer Dirichlet process TDP is used to lea...

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Problems solved by technology

[0003] In A.S.Willsky et al., entitled "Describing Visual Scenes Using Transformed Objects and Parts", the transfer Dirichlet process TDP is used to learn the category of a region in a complex scene. This method combines geometric features and textures features to improve the recognition rate, but it can only be applied to scene images from specific angles, not to multi-angle natural face images

In addition, in the paper entitled "Facial Expression Recognition via a Boosted Deep Belief Network" by P. Liu et al., the independent feature extraction, feature selection and facial expression recognition processes in traditional learning are integrated int

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