Emotion recognition of multimodal face images based on convolutional neural network

Abstract

In the background of changing angle and multi-dimensional posture overlapping, it is necessary to extract and recognize the emotion of multi-modal face images, so as to improve the expression ability of facial emotion. A method of emotion recognition of multi-modal face images under changing angle and multi-dimensional posture overlapping background based on convolution neural network and morphological feature parameter recognition is proposed. Constructing a face feature collection model with variable angle and multi-dimensional posture overlapping background, performing fusion filtering on the collected face images with variable angle and multi-dimensional posture overlapping background, extracting the edge contour feature quantity of the face images with variable angle and multi-dimensional posture overlapping background, and filtering and denoising the original image by using morphological convolution neural network transformation method, Multi-modal wavelet scale decomposition method is used to decompose the emotion features of multi-modal face images under the background of changing angles and multi-dimensional postures, and the detection model of pixel points and similarity features of multi-modal face images under the background of changing angles and multi-dimensional postures is constructed. Morphological convolution neural network transformation method is used to transform the emotion features of multi-modal face images, and edge corner detection and expression feature point clustering analysis are combined to realize the emotion recognition of multi-modal face images. The simulation results show that this method has good performance in feature extraction and clustering of multimodal facial image emotion recognition, good expression ability of facial emotion and high image quality.