Development of facial expression recognition by significant sub-region

A problem of previous research concerning facial expression recognition using the face plane is that redundant elements are also used for recognition. In this paper, we propose to develop facial expression recognition method to maximize the accuracy by using significant sub-regions on the face plane. This method consists of five steps: image acquisition, the face plane computation, the significant sub-region identification, the displacement vector computation, and classification. In order to recognize facial expression, the face plane is applied. Area on face plane is divided into 196 (14×14) sub-regions. The cross points pass though the face plane in each sub-region is counted and used as an information for computing significant level of each sub-region. By using Principal Component Analysis (PCA), significant sub-regions on the face plane are determined. The displacement vectors are used for facial expression recognition. The support vector machine is applied for classification. To test the performance of the proposed method, the experiments were done for four expressions (happiness, angry, surprise, and sadness) by using the BU3DFE database, the maximum recognition rate is 71.20 % for using 133 significant sub-regions. The results show that the redundant elements are eliminated and the accuracy is improved.