Facial expression recognition based on Gabor feature and neural network With the development of humancomputer interaction, emotional computing has gradually become a hot issue in computer vision research. Human expressions contain a wealth of information. How to make the computer fully extract facial expression information and understand human emotions is an urgent problem to be solved. The difficulty of facial expression recognition lies in facial expression extraction and facial expression classification. This paper analyzes the local features of facial expression extracted by Gabor wavelet transform and performs the various dimensional reduction on the problem of feature redundancy, which balances the feature dimension and The contribution rate of the feature. After extracting the features, a suitable BP neural network is constructed, and the extracted features are trained as a data set to the neural network to obtain a good facial expression classifier.

An initial study of feature extractionxe2x80x99s methods in facial expression recognition Facial Expression Recognition is a subbranch of Affective Computing that specializes in extracting human emotions and facial features from visual input (images and videos) and tagging them to specific emotion hierarchies. The difficulty of this task lies not only in the subjectivity of distinguishing between human emotional states but, also in the diversity of the human race and culture that influences how we humans perceive sentiments. This article aims to tackle this challenge through two distinct methods of image processing, meaning automatic, Artificial Intelligence (AI) driven feature extraction and manual feature extraction through classical approaches and compare the performance of each of them afterwards. Our conclusion is, that both methods yield noteworthy results, and each specializes in a different context.