디지털융복합연구 (Journal of Digital Convergence)

  • 제14권10호
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  • Pages.271-276
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  • 2016
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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환경변화에 강인한 눈 검출 알고리즘 성능향상 연구

Performance Improvement for Robust Eye Detection Algorithm under Environmental Changes

  • 하진관 (세종대학교 컴퓨터공학과) ;
  • 문현준 (세종대학교 컴퓨터공학과)
  • Ha, Jin-gwan (Department of Computer Science and Engineering, Sejong University) ;
  • Moon, Hyeon-joon (Department of Computer Science and Engineering, Sejong University)
  • 투고 : 2016.08.26
  • 심사 : 2016.10.20
  • 발행 : 2016.10.28
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 조명 및 Pose 등의 다양한 환경변화에 강인한 얼굴 및 눈 검출 알고리즘을 제안한다. 일반적으로 눈 검출은 얼굴검출과 동시에 수행되며 조명 및 Pose의 변화에 따라 검출 성능에 영향을 준다. 본 논문에서는 Modified Census Transform 알고리즘 사용하여 환경변화에 강인한 얼굴검출을 수행한다. 눈은 얼굴영역의 중요한 특징으로 주변의 조명 변화 및 안경 등의 다양한 요인으로 검출 성능의 저하 요인이 된다. 이러한 문제점의 해결을 위하여 Gabor transformation과 Feature from Accelerated Segment Test 알고리즘 기반의 눈 검출 알고리즘을 제안한다. 제안된 얼굴검출 알고리즘은 27.4ms의 검출속도와 98.4%의 검출율을 보이며, 눈 검출 알고리즘의 경우 36.3ms의 검출속도와 96.4%의 검출율을 보이는 것을 확인하였다.

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.

키워드

참고문헌

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