Averaged Gabor Filter Features for Facial Expression Recognition

An efficient automatic facial expression recognition method is proposed. The method uses a set of characteristic features obtained by averaging the outputs from the Gabor filter bank with 5 frequencies and 8 different orientations, and then further reducing the dimensionality by the means of principal component analysis. The performance of the proposed system was compared with the full Gabor filter bank method. The classification tasks were performed using the K-Nearest neighbor (K-NN) classifier. The training and testing images were selected from the publicly available JAFFE database. The classification results show that the average Gabor filter (AGF) provides very high computational efficiency at the cost of a relatively small decrease in performance when compared to the full Gabor filter features.