Facial Expression Recognition Based on Multiple Base Shapes

Geometric variation is one of the important components deteriorating the facial expression recognition performance. Aligning the face image to a base shape is a commonly used preprocess step to alleviate the variation. However, the assumption of single base shape can not necessarily guarantee the best performance. In this paper, we propose for the first time a facial expression recognition framework based on multiple base shapes, which aims to minimize the geometric variation between face images with the same facial expression and retain the geometric shape difference between face images with different facial expressions. For a new sample, a weighed vote based criterion is used to give the final predicted facial expression given multiple base shapes. Experimental results on CK+ (Extended Cohn-Kanade) and JAFFE (Japanese Female Facial Expression databases) show the effectiveness of proposed method.