Earticle

초록

영어

A robust and accurate real time eye tracking system has been a challenging task for many computer vision applications. Different researchers working world wide have tried various approaches to solve this problem. Although many different algorithms exist to perform eye detection, each has its own weaknesses and strengths. But so far no system / technique exists which has shown satisfactory results in all circumstances. This research work is a comparative study on the performances of algorithms – Template Matching, Skin Segmentation, Artificial Neural Network and Haar Cascade Classifier for eye recognition. All the algorithms are developed on OpenCV platform and tested on images from Mathworks Video, GTAV, Face Expression and VITS database in the laboratory. The comparison is done based on the success rate i.e. total number of images with eyes detected to the total number of input images. The comparison results show that Haar Cascade Classifier has satisfactory results on images under different conditions such as tilted head position, closed eyes, occluded face, etc., .The purpose of this research work is to develop a Non-intrusive Driver's Drowsiness detection system based on eye blink rate for preventing accidents on road.

목차

Abstract
 1. Introduction
 2. Proposed Methods
  2.1 Template Matching Method
  2.2 Skin Segmentation Method
  2.3 Neural Network Method
  2.4 Haar Cascade Classifier Method
 3. Results
 4. Comparison of Methods
 5. Conclusion
 References

키워드

저자

• Vijayalaxmi [ Vignan Institute of Technology & Science Vignan Hills, Nalgonda District, Hyderabad, Andhra Pradesh, India ]
• D. Elizabeth Rani [ Gitam Institute of Technology, Gitam University, Vishakapatnam, Andhra Pradesh, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605