Earticle

초록

영어

Eye detection and tracking can be used in intelligent human-computer interfaces, driver drowsiness detection, security, and biology systems. In this paper a new method for eye detection based on some new rectangle features is proposed, with these features the Adaboost cascade classifiers are trained for eye detection. Then with the characteristics of symmetry of the eyes some of the geometric characteristics are adopted for correction. The geometric characteristics improve the accuracy of the eye detection, and make the rough cascade classifier trained by few samples become a reality in application. In this paper, we present an integrated eye tracker to overcome the effect of eye closure and external illumination by combining Kalman filter with Mean Shift algorithm. Results from an extensive experiment show a significant improvement of our technique over existing eye tracking techniques.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Eye Detection Using Rectangle Feature and Geometric Characters
  3.1. Rectangle Features Design
  3.2. Cascade Classifier by Adaboost
  3.3. Geometric Correction for Precise Positioning
 4. Eye Tracking by using Integrated Eye Tracker
 5. Experimental Results
 6. Conclusion
 References

키워드

저자

• Wenkai Xu [ Department of Information & Communications Engineering, Tongmyong University, Busan 608-711, Korea ]
• Eung-Joo Lee [ Department of Information & Communications Engineering, Tongmyong University, Busan 608-711, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605