Recognition of occluded facial expressions based on CENTRIST features
Emotion recognition based on facial expressions plays an important role in numerous applications, such as affective computing, behavior prediction, human-computer interactions, psychological health services, interpersonal relations, and social monitoring. In this work, we describe and analyze an emotion recognition system based on facial expressions robust to occlusions through Census Transform Histogram (CENTRIST) features. Initially, occluded facial regions are reconstructed by applying Robust Principal Component Analysis (RPCA). CENTRIST features are extracted from the facial expression representation, as well as Local Binary Patterns (LBP), Local Gradient Coding (LGC) and an extended Local Gradient Coding (LGC-HD). Then, the feature vector is reduced through Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). For facial expression recognition, K-nearest neighbor (K-NN) and Support Vector Machine (SVM) classifiers are applied and tested. Experimental results on two public data sets demonstrated that the CENTRIST representation achieved competitive accuracy rates for occluded and non-occluded facial expressions compared to other state-of-the-art approaches available in the literature.