Suitability of Genetic Algorithm and Particle Swarm Optimization For Eye Tracking System

被引：4
|
作者：
Amudha, J. [1 ]
Chandrika, K. R. [1 ]
机构：
[1] Amrita Vishwa Vidyapeetham, Amrita Sch Engn, Bangalore, Karnataka, India
来源：
2016 IEEE 6TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING (IACC) | 2016年
关键词：
Eye Detection; Eye Tracking; Genetic Algorithm; Particle Warm Optimization;
D O I：
10.1109/IACC.2016.56
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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 %.
引用
收藏
页码：256 / 261
页数：6
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