Earticle

초록

영어

As one of the most salient features of human face, eyes play an important role in interpreting and understanding a person’s desires, needs, and emotional states. In this paper, a robust real-time “coarse to fine” method based on combining the appearance-based method and SVM approach was proposed for eye detection firstly, which combining the respective strengths of different complimentary techniques and overcoming their shortcomings. And then, a complementary tracker was designed for tracking eyes, which the eye model is continuously updated by having the eye successfully detected from the last Kalman tracker, to avoid error propagation with the CamShift tracker. Experimental results show that these enhancements have led to a significant improvement in eye tracking robustness and accuracy over existing eye trackers, especially under various conditions identified.

목차

Abstract
 1. Introduction
 2. Related Previous Works
  2.1. Haar Features and Integral Image for Face Detection
  2.2. Support Vector Machines Approach
 3. Eye Detection based on Haar Feature and SVM Approach
  3.1. New Haar Features for Rough Eye Detection
  3.2. Eye Detection Using Support Vector Machines
  3.3. Support Vector Machines Training State
 4. Eye Tracking using a Combining Tracker
  4.1. Eye Tracker with Kalman Filter
  4.2. Eye Tracker combining CamShif Approach
 5. Experimental Results
  5.1. Experimental Results of Eye Detecti
  5.2. Experimental Results of Combining Eye Tracker
 6. Conclusion
 References

키워드

저자

• Kun Mu [ Department of Computer Science and Engineering, Henan Institute of Engineering, Zhengzhou 451191, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605