대한전자공학회논문지SP (Journal of the Institute of Electronics Engineers of Korea SP)

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

그래디언트와 상관관계의 국부통계를 이용한 얼굴 인식

Face Recognition Using Local Statistics of Gradients and Correlations

  • 구영애 (경북대학교 IT대학 전자공학부) ;
  • 소현주 (경북대학교 IT대학 전자공학부) ;
  • 김남철 (경북대학교 IT대학 전자공학부)
  • Ju, Yingai (School of Electronics Engineering, College of IT Engineering, Kyungpook National University) ;
  • So, Hyun-Joo (School of Electronics Engineering, College of IT Engineering, Kyungpook National University) ;
  • Kim, Nam-Chul (School of Electronics Engineering, College of IT Engineering, Kyungpook National University)
  • 투고 : 2010.09.08
  • 심사 : 2011.02.14
  • 발행 : 2011.05.25
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초록

지금까지 많은 얼굴 인식 방법들이 제안되었으나, 대부분의 방법들은 특징추출 과정 없이 입력 영상을 1차원 형태의 벡터로 변형한 것을 1차원 특징 벡터로 사용하거나 또는 입력 영상 자체를 특징 매트릭스로 사용하였다. 이와같이 영상 자체를 특징으로 사용하면 조명변화가 심한 데이터베이스에서는 성능이 좋지 않는 것으로 알려져 있다. 본 논문에서는 조명변화에 효과적인 그래디언트와 상관관계의 국부통계를 이용하여 얼굴을 인식하는 방법을 제안하였다. BDIP(block difference of inverse probabilities)는 그래디언트의 국부 통계이다. 그리고 BVLC(block variation of local correlation coefficients)의 두 타입은 상관관계의 국부 통계이다. 입력영상이 얼굴인식 시스템에 들어 오면 먼저 BDIP, BVLC1, BVLC2의 특징 영상을 추출하고 융합한 후, (2D)2 PCA 변환을 거쳐 특징 매트릭스를 얻어서 훈련특징 매트릭스와의 거리를 구하여 최근린 분류기를 이용하여 얼굴 영상을 인식한다. 네 가지 얼굴 데이터베이스, FERET, Weizmann, Yale B, Yale에 대한 실험결과로부터, 제안한 방법이 실험한 여섯 가지 방법 중에서 조명과 얼굴 표정의 변화에 가장 견실하다는 것을 알 수 있었다.

Until now, many face recognition methods have been proposed, most of them use a 1-dimensional feature vector which is vectorized the input image without feature extraction process or input image itself is used as a feature matrix. It is known that the face recognition methods using raw image yield deteriorated performance in databases whose have severe illumination changes. In this paper, we propose a face recognition method using local statistics of gradients and correlations which are good for illumination changes. BDIP (block difference of inverse probabilities) is chosen as a local statistics of gradients and two types of BVLC (block variation of local correlation coefficients) is chosen as local statistics of correlations. When a input image enters the system, it extracts the BDIP, BVLC1 and BVLC2 feature images, fuses them, obtaining feature matrix by $(2D)^2$ PCA transformation, and classifies it with training feature matrix by nearest classifier. From experiment results of four face databases, FERET, Weizmann, Yale B, Yale, we can see that the proposed method is more reliable than other six methods in lighting and facial expression.

키워드

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