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Performance Improvement for Robust Eye Detection Algorithm under Environmental Changes

환경변화에 강인한 눈 검출 알고리즘 성능향상 연구

  • Ha, Jin-gwan (Department of Computer Science and Engineering, Sejong University) ;
  • Moon, Hyeon-joon (Department of Computer Science and Engineering, Sejong University)
  • 하진관 (세종대학교 컴퓨터공학과) ;
  • 문현준 (세종대학교 컴퓨터공학과)
  • Received : 2016.08.26
  • Accepted : 2016.10.20
  • Published : 2016.10.28

Abstract

In this paper, we propose robust face and eye detection algorithm under changing environmental condition such as lighting and pose variations. Generally, the eye detection process is performed followed by face detection and variations in pose and lighting affects the detection performance. Therefore, we have explored face detection based on Modified Census Transform algorithm. The eye has dominant features in face area and is sensitive to lighting condition and eye glasses, etc. To address these issues, we propose a robust eye detection method based on Gabor transformation and Features from Accelerated Segment Test algorithms. Proposed algorithm presents 27.4ms in detection speed with 98.4% correct detection rate, and 36.3ms face detection speed with 96.4% correct detection rate for eye detection performance.

Keywords

Modified Census Transform;Gabor Transform;Features from Accelerated Segment Test;Face Detection;Eye Detection;Pupil Detection

Acknowledgement

Supported by : 농림수산식품기술기획평가원

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