Multi-pose facial expression recognition method based on generative adversarial network

Abstract

The invention discloses a multi-pose facial expression recognition method based on a generative adversarial network. The multi-pose facial expression recognition method based on a generative adversarial network comprises: adding a front face synthesis module to an expression recognition system under the multi-face posture in the expression recognition process, inputting a face detected by the system and a synthesized front face into a recognition network at the same time, to improve the recognition performance under large-posture deflection of the face, and therefore expression recognition under various face deflection postures is achieved. The beneficial effects of the multi-face posture expression recognition system of front face synthesis module constructed based on proposed generativeadversarial network mainly are that: 1, the front face of the original image can be synthesized by the aid of the front face synthesis module based on the generative adversarial network through the input human face at any angle, front face information is provided for an expression recognition system, and correct recognition of expression information during large-posture deflection of the human face is guaranteed;

Description

technical field [0001] The invention relates to the field of facial expressions, in particular to a multi-pose facial expression recognition method based on a generative confrontation network. Background technique [0002] Expressions are among the most powerful, natural, and ubiquitous signals humans use to express emotional states and intentions. Facial expression analysis has been intensively studied due to its practical importance in social robotics, medical therapy, driver fatigue monitoring, and many other human-computer interaction systems. As early as the 20th century, relevant researchers basically divided human facial expressions into seven categories, namely "fear, anger, disgust, happiness, normal, sad, surprise". In the fields of computer vision and machine learning, various systems for facial expression recognition have been developed to encode expression information from facial expressions. In recent years, artificial intelligence technology represented by d...

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Problems solved by technology

[0008] The technical problem to be solved by the present invention is to provide a multi-pose facial expression recognition method based on generative confrontation network, and solve the problem that the frontal face cannot be captured due to facial angle deflection through deep learning technology. The realized system should be deflected at different facial angles Realize the synthesis of the front of the face under normal circumstances, and retain the original identity and expression information; design and implement the expression recognition classifier based on the original face and the synthetic front face, and realize the expression recognition under the large posture face deflection; simplify the system model to ensure System performance

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