PCANet-CNN-based arbitrary attitude facial expression recognition method

Abstract

The invention discloses a PCANet-CNN-based arbitrary attitude facial expression recognition method. The method comprises the following steps: firstly pre-processing the original images to obtain gray level facial images with uniform size, wherein the gray level facial images comprise front facial images and side facial images; inputting the front face images into an unsupervised characteristic learning model PCANet and learning to obtain characteristics corresponding to the front facial images; inputting the side facial images into a supervised characteristic learning model CNN, and training by taking the front facial characteristics obtained through the unsupervised characteristic learning as labels so as to obtain a mapping relationship between the front facial characteristics and the side facial characteristics; and obtaining uniform front facial characteristics corresponding to the facial images at arbitrary attitudes through the mapping relationship, and finally sending the uniform front facial characteristics into SVM to train so as to obtain a uniform recognition model in allusion to arbitrary attitudes. According to the method provided by the invention, the problem of low model recognition rate caused by the condition of respectively modeling for each attitude in the traditional multi-attitude facial expression recognition and the factors such as attitude and the like is solved, and the correctness of the multi-attitude facial image expression recognition can be effectively improved.

Description

technical field [0001] The invention belongs to the field of emotion recognition, and in particular relates to a PCANet-CNN-based method and system for recognizing facial expressions in arbitrary poses Background technique [0002] Facial expression recognition is an important research direction in the fields of pattern recognition, human-computer interaction and computer vision, and has become a research hotspot at home and abroad. Generally speaking, the six most common basic human expressions are happiness, sadness, anger, surprise, disgust and fear. In recent years, with the continuous proposal of various features that are robust to poses, the development of multi-pose automatic facial expression recognition technology has been promoted. For example, the traditional face recognition model can only perform facial expression recognition based on pictures of frontal or near-frontal faces, but the recognition effect on facial expressions of side faces or faces with a certai...

AI Technical Summary

Problems solved by technology

First of all, this method only uses key points to learn the mapping relationship between frontal face images and side face images, and the information is insufficient; secondly, its pose normalization and feature extraction are completed in two separate steps, which reduces the model recognition rate and efficiency

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