Facial expression recognition using three-stage support vector machines

Abstract

Herein, a three-stage support vector machine (SVM) for facial expression recognition is proposed. The first stage comprises 21 SVMs, which are all the binary combinations of seven expressions. If one expression is dominant, then the first stage will suffice; if two are dominant, then the second stage is used; and, if three are dominant, the third stage is used. These multilevel stages help reduce the possibility of experiencing an error as much as possible. Different image preprocessing stages are used to ensure that the features attained from the face detected have a meaningful and proper contribution to the classification stage. Facial expressions are created as a result of muscle movements on the face. These subtle movements are detected by the histogram-oriented gradient feature, because it is sensitive to the shapes of objects. The features attained are then used to train the three-stage SVM. Two different validation methods were used: the leave-one-out and K-fold tests. Experimental results on three databases (Japanese Female Facial Expression, Extended Cohn-Kanade Dataset, and Radboud Faces Database) show that the proposed system is competitive and has better performance compared with other works.