Hexagonal Descriptor Particle Swarm Optimization with Knowledge-Crowding for Face Recognition

Abstract

Automatic identification of various facial 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 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. Face recognition is done 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 method Hexagonal Descriptor Particle Swarm Optimization with Knowledge-Crowding (HDPSO-KC) overcomes from local optima and improves global search process and collaborative work. The method also covers the problem of eliminating the particles in denser regions in Pareto front distribution. The proposed methodology is validated with benchmark datasets for analyzing the performance over other methods.