Facial expression recognition with local prominent directional pattern

Local edge-based descriptors have gained much attention as feature extraction methods for facial expression recognition. However, such descriptors are found to suffer from unstable shape representations for different local structures for their sensitivity to local distortions such as noise and positional variations. We propose a novel edge-based descriptor, named Local Prominent Directional Pattern (LPDP), which considers statistical information of a pixel neighborhood to encode more meaningful and reliable information than the existing descriptors for feature extraction. More specifically, LPDP examines a local neighborhood of a pixel to retrieve significant edges corresponding to the local shape and thereby ensures encoding edge information in spite of some positional variations and avoiding noisy edges. Thus LPDP can represent important textured regions much effectively to be used in facial expression recognition. Extensive experiments on facial expression recognition on well-known datasets also demonstrate the better capability of LPDP than other existing descriptors in terms of robustness in extracting various local structures originated by facial expression changes.