Facial expression recognition based on local binary patterns

In this paper, a novel approach to automatic facial expression recognition from static images is proposed. The face area is first divided automatically into small regions, from which the local binary pattern (LBP) histograms are extracted and concatenated into a single feature histogram, efficiently representing facial expressions—anger, disgust, fear, happiness, sadness, surprise, and neutral. Then, a linear programming (LP) technique is used to classify the seven facial expressions. Experimental results demonstrate an average expression recognition accuracy of 93.8% on the JAFFE database, which outperforms the rate of all other reported methods on the same database.