Local weighted Pseudo Zernike Moments and fuzzy classification for facial expression recognition

Recently, various approaches to facial expression recognition have been proposed, but they do not provide a powerful approach to recognize expressions from 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 Pseudo Zernike Moments (LWPZM) and fuzzy classification. Pseudo Zernike Moments (PZM) are 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, we use fuzzy inference system for classify facial expressions. An extensive experimental investigation is conducted using Radboud Faces database. The encouraging experimental results demonstrate that the proposed method has significant improvement than other methods.