Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Face Recognition using Fuzzy-EBGM(Elastic Bunch Graph Matching) Method

Fuzzy Elastic Bunch Graph Matching 방법을 이용한 얼굴인식

  • Kwon Mann-Jun (Chungbuk National University School of Electrical and Computer Engineering) ;
  • Go Hyoun-Joo (Chungbuk National University School of Electrical and Computer Engineering) ;
  • Chun Myung-Geun (Chungbuk National University School of Electrical and Computer Engineering)
  • 권만준 (충북대학교 전기전자컴퓨터공학부) ;
  • 고현주 (충북대학교 전기전자컴퓨터공학부) ;
  • 전명근 (충북대학교 전기전자컴퓨터공학부)
  • Published : 2005.12.01
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Abstract

In this paper we describe a face recognition using EBGM(Elastic Bunch Graph Matching) method. Usally, the PCA and LDA based face recognition method with the low-dimensional subspace representation use holistic image of faces, but this study uses local features such as a set of convolution coefficients for Gabor kernels of different orientations and frequencies at fiducial points including the eyes, nose and mouth. At pre-recognition step, all images are represented with same size face graphs and they are used to recognize a face comparing with each similarity for all images. The proposed algorithm has less computation time due to simplified face graph than conventional EBGM method and the fuzzy matching method for calculating the similarity of face graphs renders more face recognition results.

본 논문은 EBGM(Elastic Bunch Graph Matching)기법을 이용한 얼굴인식에 대해 다룬다. 대용량 영상 정보에 대해 차원 축소를 이용한 얼굴인식 기법인 주성분기법이나 선형판별기법에서는 얼굴 영상 전체의 정보를 이용하는 반면 본 연구에서는 얼굴의 눈 코 입 등과 같은 얼굴 특징점에 대해 주파수와 방향각이 다른 여러 개의 가버 커널과 영상 이미지의 컨볼루션(Convolution)의 계수의 집합(Jets)을 이용한 특징 데이터를 이용한다. 하나의 얼굴 영상에 대해서 모든 영상이 같은 크기의 특질 데이터로 표현되는 Face Graph가 생성되며, 얼굴인식 과정에서는 추출된 제트의 집합에 대해서 상호 유사도(Similarity)의 크기를 비교하여 얼굴인식을 수행한다. 본 논문에서는 기존의 EBGM 방법의 Face Graph 생성 과정을 보다간략화 한 방법을 이용하여 얼굴인식 과정에서 계산량을 줄여 속도를 개선하였으며, 퍼지 매칭법을 이용한 유사도 계산을 하였다.

Keywords

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