Facial expression recognition with occlusions based on geometric representation

Abstract

In recent years, emotion recognition based on facial expressions has received increasing attention by the scientific community in several knowledge domains, such as emotional analysis, pattern recognition, behavior prediction, interpersonal relations, human-computer interactions, among others. This work describes an emotion recognition system based on facial expressions robust to occlusions. Initially, the occluded facial expression to be recognized is reconstructed through Robust Principal Component Analysis (RPCA). Then, a fiducial point detection is performed to extract facial expression features, represented by Gabor wavelets and geometric features. The feature vector space is reduced using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Finally, K-nearest neighbor algorithm (K-NN) and Support Vector Machine (SVM) classifiers are used to recognize the expressions. Three public data sets are used to evaluate our results. The geometric representation achieved high accuracy rates for occluded and nonoccluded faces compared to approaches available in the literature.