전자공학회논문지CI (Journal of the Institute of Electronics Engineers of Korea CI)

대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지

조명 변화 환경에서 이진패턴 영상을 이용한 얼굴인식 방법에 관한 연구

A Study on Face Recognition Method based on Binary Pattern Image under Varying Lighting Condition

  • 김동주 (대구경북과학기술원 IT융합연구부) ;
  • 손명규 (대구경북과학기술원 IT융합연구부) ;
  • 이상헌 (대구경북과학기술원 IT융합연구부)
  • Kim, Dong-Ju (Division of IT Convergence, Daegu Gyeongbuk Institute of Science & Technology) ;
  • Sohn, Myoung-Kyu (Division of IT Convergence, Daegu Gyeongbuk Institute of Science & Technology) ;
  • Lee, Sang-Heon (Division of IT Convergence, Daegu Gyeongbuk Institute of Science & Technology)
  • 투고 : 2011.02.16
  • 심사 : 2012.03.05
  • 발행 : 2012.03.25
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초록

본 논문에서는 MCS-LBP 이진패턴 영상과 2D-PCA 알고리즘을 이용한 조명 변화에 강인한 얼굴인식 시스템에 대하여 제안한다. 이진패턴 변환은 기존의 얼굴인식 및 표정인식 분야에 사용되는 기법으로, 일반적으로 조명 변화에 강인한 특성을 갖는다. 이에 본 논문에서는 기존의 LBP보다 조명 변화에 더 강인한 MCS-LBP를 제안하고, 더불어 2D-PCA 알고리즘과 결합하는 얼굴인식 시스템을 제안한다. 제안하는 얼굴인식 방법의 성능평가는 기존의 다양한 이진패턴 변환 영상과 얼굴인식에 널리 사용되고 있는 PCA, LDA, 2D-PCA 및 가버영상의 ULBP 히스토그램 특징을 사용하여 수행하였다. 다양한 조명변화 환경에서 구축된 YaleB, extended YaleB, CMU-PIE 등의 공인 얼굴 데이터베이스를 이용하여 실험한 결과, 제안하는 MCS-LBP영상과 2D-PCA 특징을 사용한 방법이 가장 우수한 인식 성능을 보였다.

In this paper, we propose a illumination-robust face recognition system using MCS-LBP and 2D-PCA algorithm. A binary pattern transform which has been used in the field of the face recognition and facial expression, has a characteristic of robust to illumination. Thus, this paper propose MCS-LBP which is more robust to illumination than previous LBP, and face recognition system fusing 2D-PCA algorithm. The performance evaluation of proposed system was performed by using various binary pattern images and well-known face recognition features such as PCA, LDA, 2D-PCA and ULBP histogram of gabor images. In the process of performance evaluation, we used a YaleB face database, an extended YaleB face database, and a CMU-PIE face database that are constructed under varying lighting condition, and the proposed system which consists of MCS-LBP image and 2D-PCA feature show the best recognition accuracy.

키워드

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