Robust facial expression recognition system based on hidden Markov models

Facial expressions are considered important communicative tools. In this paper, we present a new system able to recognize facial expressions. This system utilizes one-dimensional Hidden Markov Models (1D-HMMs) with small number of states involving fast computational time during the learning and recognition steps. First, face features are obtained using Gabor wavelets. Then, the Fisher’s Discriminant Analysis method is employed to reduce the features dimensions to eliminate redundancy and overlap in the information. The proposed system differs from previous methods using 1D-HMMs, by the fact that it employs 1D-HMMs with only three states without any prior segmentation step of interest regions in the face (hair, forehead, eyes, etc). The proposed system is evaluated using the JAFFE and KDEF facial expressions data sets.