Automatic Analysis of Human Facial Expression Recognition in Image Sequence
Facial expression conveys nonverbal cues among humans. Recognition of facial expression is an essential research area in the field of Human Machine Interfaces (HMIs) Most of the existing automated system for facial expression analysis has an impact over accuracy. This paper proposes Multi class Linear Discriminant Analysis (LDA) to obtain better recognition rate of facial expressions. The seven basic facial expressions are used in this work are happy, surprise, sad, anger, fear, disgust, and neutral. The face in an image sequence is detected using Viola Jones algorithm. Geometric and Appearance based feature extraction approaches are used here. The geometrical features are extracted using Image Normalization and Thresholding techniques and optimal facial feature points are selected by an Entropy calculation. Local Directional Pattern (LDP) is utilized to extract the appearance based facial feature which contains edge, spots and corner information of an image, and then the Local Directional Pattern Variance (LDPv) descriptor obtains contrast contribution during expression changes. Finally, the performance of the system is compared with machine learning techniques. The proposed method is trained and tested using images from Cohn Kanade database.