Recognition of facial expressions using locally weighted and adjusted order Pseudo Zernike Moments

Recently, various approaches to facial expression recognition have been proposed, but they do not provide a powerful approach to recognize expressions from Partially Occluded Facial Images. Moreover, they usually are global and the importance of different areas in facial images is considered equally. In this paper, we propose a novel facial expression recognition approach based on locally weighted and adjusted order Pseudo Zernike Moments (PZM). PZM is one of the best descriptors that are robust to noise and rotation. In our system, the proposed method employs a local PZM to represent faces partitioned into patches. Also, in this paper, the maximum order of PZM is adjusted based on the importance of the local areas. An extensive experimental investigation is conducted using JAFFE, FG-Net and Radboud Faces databases. The encouraging experimental results demonstrate that the proposed method has significant improvement than other methods. Moreover, our system is robust to the changes on age, ethnicity, and gender.