Facial decomposition for expression recognition using texture/shape descriptors and SVM classifier

Automatic facial expression analysis is a challenging topic in computer vision due to its complexity and its important role in many applications such as human-computer and social interaction. This paper presents a Facial Expression Recognition (FER) method based on an automatic and more efficient facial decomposition into regions of interest (ROI). First, seven ROIs, representing more precisely facial components involved in expression of emotions (left eyebrow, right eyebrow, left eye, right eye, between eyebrows, nose and mouth), are extracted using the positions of some landmarks detected by IntraFace (IF). Then, each ROI is resized and partitioned into blocks which are characterized using several texture and shape descriptors and their combination. Finally, a multiclass SVM classifier is used to classify the six basic facial expressions and the neutral state. In term of evaluation, the proposed automatic facial decomposition is compared with existing ones to show its effectiveness, using three public datasets. The experimental results showed the superiority of our facial decomposition against existing ones and reached recognition rates of 96.06%, 92.03% and 93.34% for the CK, FEED and KDEF datasets, respectively. Then, a comparison with state-of-the-art methods is carried out using CK+ dataset. The comparison analysis demonstrated that our method outperformed or competed the results achieved by the compared methods.