Robust Facial Expression Recognition Using Revised Canonical Correlation

The poor alignment and large variations in the temporal sale of facial expressions are two crucial problems for facial expression recognition (FER). Canonical correlation (CC) has recently received increasing attention in surveillance face recognition because of its robustness to variations of alignment. But it could not suit well to FER, because the facial expression variations and temporal information are ignored for canonical subspace. This paper proposes the revised canonical correlation method to address the two above described issues for making FER be robust to false detection or mis-alignment. Firstly, this paper presents the local binary pattern to describe the appearance features for enhancing the spatial variations of facial expression. Secondly, this paper proposes the temporal orthogonal locality preserved projection for building a canonical subspace of a video clip, where it mostly captures the motion changes of facial expressions. Then this paper presents the discriminative CC to model the low-dimensional feature space, which increases robustness to imprecise alignment and strengthens discrimination for facial expressions. Extensive experimental results on Extended Cohn-Kanade and MAHNOB-HCI databases demonstrate that the proposed method achieves the best results in recognizing facial expressions and performs robustly with ordinary on general face detection and eye detection.