Publication Type:

Conference Paper


2016 IEEE 6th International Conference on Advanced Computing (IACC), IEEE, p.256-261 (2016)





Biological cells, Deformable template matching, Evolutionary algorithms, Eye detection, Eye Tracking, eye tracking system, face, gaze tracking, Genetic algorithm, Genetic algorithms, Haar transforms, Haar-like features, Image matching, particle swarm optimisation, particle swarm optimization, Particle Warm Optimization, Real-time systems, Tracking


Evolutionary algorithms provide solutions to optimization problem and its suitability to eye tracking is explored in this paper. In this paper, we compare the evolutionary methods Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) using deformable template matching for eye tracking. Here we address the various eye tracking challenges like head movements, eye movements, eye blinking and zooming that affect the efficiency of the system. GA and PSO based Eye tracking systems are presented for real time video sequence. Eye detection is done by Haar-like features. For eye tracking, GAET and PSOET use deformable template matching to find the best solution. The experimental results show that PSOET achieves tracking accuracy of 98% in less time. GAET predicted eye has high correlation to actual eye but the tracking accuracy is only 91 %.


cited By 0; Conference of 6th International Advanced Computing Conference, IACC 2016 ; Conference Date: 27 February 2016 Through 28 February 2016; Conference Code:123372

Cite this Research Publication

J. Amudha and Chandrika, K. R., “Suitability of Genetic Algorithm and Particle Swarm Optimization for Eye Tracking System”, in 2016 IEEE 6th International Conference on Advanced Computing (IACC), 2016, pp. 256-261.