An approach for behavior analysis using correlation spectral embedding method

Automatic identification of various facial movements and expressions with high recognition value is important for human computer interaction as the facial behavior of a human can be treated as an important factor for information representation as well as communication. A high deviation of human appearance and existence of noisy contextual background makes the human pose analysis is hard to achieve. A number of basic factors such as cluttered background, occlusion, and camera movement and illumination variations degrade the image quality resulting in poor performance for identifying different facial expressions. Moreover, the identification of the automatic feature detection in facial behavior requires high degree of correlation between the training and test images. Our proposed work tries to address the mentioned problems and resolve to some extent. In this methodology, a Decision-based Spectral Embedding approach combining appearance and geometry based features for head pose estimation and facial expression recognition by minimizing the objective function which leads to selection of optimal set of fiducial points. The method preserves the local information from different facial views for mapping neighboring input to its corresponding output, resulting in low dimensional representation for encoding the relationships of the data. The proposed methodology is validated with benchmark datasets for analyzing the performance of recognition of facial behavior.