Human face expression intensity recognition method and system based on hidden variable analysis

Abstract

The invention discloses a human face expression intensity recognition method and system based on hidden variable analysis. According to the method, a twin convolutional neural network structure is adopted, face features extracted from the front end of a convolutional neural network are divided into orthogonal identity subspaces and expression subspaces through a hidden variable analysis method, the identity subspace feature difference of two branches of the twin network is minimized, the identity features and the expression features can be effectively separated; and meanwhile, sequencing constraint and semi-supervised regression training based on a time sequence are carried out on the expression subspace to obtain an expression intensity recognition model which can be used for continuous facial expression intensity recognition. According to the method, hidden variable analysis is used for expression intensity recognition, face features are divided into identity-related features and expression-related features, interference of identity information on expression intensity information is suppressed, and the robustness of expression intensity recognition can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of computer vision and pattern recognition, and more specifically relates to a method and system for recognizing facial expression intensity based on latent variable analysis. Background technique [0002] Facial expressions play a very important role in people's emotional communication. Analysis of facial expressions includes expression recognition and expression strength recognition. The work of expression recognition is mainly to distinguish six types of basic expressions according to the type of expression, including: anger, disgust, fear, happiness, sadness, and surprise; while expression intensity recognition further distinguishes the subtle differences in intensity between similar expressions. Just identifying the category of expressions is not a good way to understand people's emotions, and it is also necessary to decipher their meaning through changes in the intensity of expressions. [0003] Exis...

AI Technical Summary

Problems solved by technology

[0004] Continuous expression intensity recognition is easily interfered by facial identity information. Some existing technologies propose a method of introducing a neutral reference frame, which can effectively solve this problem

However, this method is limited in its application, i.e. a neutral reference frame must be available during both training and testing

Images