Face recognition under varying facial expression based on Perceived Facial Images and local feature matching

Face recognition is becoming a difficult process because of the generally similar shapes of faces and because of the numerous variations between images of the same face. A face recognition system aims at recognizing a face in a manner that is as independent as possible of these image variations. Such variations make face recognition, on the basis of appearance, a difficult task. This paper attempts to overcome the variations of facial expression and proposes a biological vision-based facial description, namely Perceived Facial Images (PFIs), applied to facial images for 2D face recognition. Based on the intermediate facial description, SIFT-based feature matching is then carried out to calculate similarity measures between a given probe face and the gallery ones. Because the proposed biological vision-based facial description generates a PFI for each quantized gradient orientation of facial images, we further propose a weighted sum rule based fusion scheme. The proposed approach was tested on three facial expression databases: the Cohn and Kanade Facial Expression Database, the Japanese Female Facial Expression (JAFFE) Database and the FEEDTUM Database. The experimental results demonstrate the effectiveness of the proposed method.