Facial Expression Bilinear Encoding Model

Haifeng Zhang, Wen Su, Zengfu Wang

Abstract: Facial expressions are generated by contractions of facial muscles. The contractions lead to variations in the appearance of facial parts. It has been proved that the features from different facial parts can improve the accuracy of facial expression recognition. In this paper, we propose a bilinear encoding model for facial expression recognition. Our system uses the still facial expression images as inputs and employs the bilinear convolutional networks to capture the features in the appearance of facial parts. It detects crucial facial parts and extracts the appearance features simultaneously with end-to-end learning. To verify the performance of our system, we have made experiments on two popular expression databases: CK+ and Oulu-CASIA. The experimental results show that the proposed method achieves comparable or better performance compared with the state-of-the-art methods for facial expression recognition.