An Efficient Eye Detection and Tracking System based on Particle Swarm

Eye detection and tracking are challenging due to a variety of factors such as eye-blinking, partially closed eyes, and oblique face orientations which tend to significantly limit the efficiency of most eye trackers. In this research, an efficient eye detection and tracking system is presented to overcome these limitations. The proposed system switches between the particle swarm optimization (PSO) based deformable multiple template matching algorithm and the adaptive block-matching search algorithm to improve the efficiency and robustness of the tracking system. For eye detection, PSO-based deformable multiple template matching is employed to estimate the best candidate of the center of the eyes within an image of the video sequence with the highest accuracy. For eye tracking the block-matching algorithm with adaptive search area is utilized to reduce the computational time required to perform the PSO-based algorithm. Experimental results on the standard VidTIMIT database show that the proposed method outperforms competitive algorithms in terms of of accuracy and computational complexity.

Autorentext

Dr. Rehab F. Abdel-Kader received the Ph.D. degree in Computer Science and Engineering from Auburn University in 2003. She is currently an Associate Professor at the Electrical Engineering Department, Faculty of Engineering, Port-Said University. Research interests include Artificial Intelligence and Computer Vision.