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Abstract

Facial expression recognition plays a significant role in human-computer interaction, social interaction, perception, social intelligence, educational scenarios, E-Learning environments etc. Automatic detection of facial expressions of a person can help in providing better human-computer interface. In this paper, a novel framework is presented to recognize the facial expressions, which enhances the efficiency and speed of recognition system by extracting significant features of a face. In the proposed framework, feature representation and extraction are done by using Median Robust Extended Local Binary Patterns (MRELBP) and Histogram of Oriented Gradients (HOG). Later, the dimensionalities of the obtained features are reduced using Compressive Sensing (CS) algorithm and classified using multiclass SVM classifier. We investigated the performance of the proposed framework on two public databases such as CK+ and JAFFE data sets. The investigational results show that the proposed framework is robust for recognizing facial expressions with varying illuminations and poses in real time.