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

The Institute of Electronics and Information Engineers (대한전자공학회)
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Class Discriminating Feature Vector-based Support Vector Machine for Face Membership Authentication

얼굴 등록자 인증을 위한 클래스 구별 특징 벡터 기반 서포트 벡터 머신

  • Kim, Sang-Hoon (School of Electronic Engineering, Soongsil University) ;
  • Seol, Tae-In (School of Electronic Engineering, Soongsil University) ;
  • Chung, Sun-Tae (School of Electronic Engineering, Soongsil University) ;
  • Cho, Seong-Won (Department of Electronic and Electrical Engineering, Hongik University)
  • 김상훈 (숭실대학교 정보통신전자공학부) ;
  • 설태인 (숭실대학교 정보통신전자공학부) ;
  • 정선태 (숭실대학교 정보통신전자공학부) ;
  • 조성원 (홍익대학교 전자전기공학부)
  • Published : 2009.01.25
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Abstract

Face membership authentication is to decide whether an incoming person is an enrolled member or not using face recognition, and basically belongs to two-class classification where support vector machine (SVM) has been successfully applied. The previous SVMs used for face membership authentication have been trained and tested using image feature vectors extracted from member face images of each class (enrolled class and unenrolled class). The SVM so trained using image feature vectors extracted from members in the training set may not achieve robust performance in the testing environments where configuration and size of each class can change dynamically due to member's joining or withdrawal as well as where testing face images have different illumination, pose, or facial expression from those in the training set. In this paper, we propose an effective class discriminating feature vector-based SVM for robust face membership authentication. The adopted features for training and testing the proposed SVM are chosen so as to reflect the capability of discriminating well between the enrolled class and the unenrolled class. Thus, the proposed SVM trained by the adopted class discriminating feature vectors is less affected by the change in membership and variations in illumination, pose, and facial expression of face images. Through experiments, it is shown that the face membership authentication method based on the proposed SVM performs better than the conventional SVM-based authentication methods and is relatively robust to the change in the enrolled class configuration.

얼굴 등록자 인증은 얼굴 인식을 기반으로 인증하고자 하는 사람이 등록자인지, 아닌지를 판별하는 것으로, 기본적으로 2클래스 분류 문제이다. 서포트 벡터 머신(Support Vector Machine, 이하 SVM)은 2 클래스 분류 문제에 효과적인 것으로 잘 알려져 있다. 얼굴 등록자 인증의 분류에 사용되었던 기존의 SVM들은 각 클래스 (등록자 클래스, 미등록자 클래스) 구성원의 얼굴 이미지로부터 추출된 이미지 특징 벡터를 이용하여 훈련되고 인증된다. 이렇게 훈련 세트 구성원들의 이미지 특징 벡터들로 훈련된 SVM은 인증시의 얼굴 이미지가 SVM 훈련 세트의 얼굴 이미지들의 조명, 자세, 표정들과 다른 인증 환경의 경우나 등록자의 가입 및 탈퇴 등으로 등록 클래스나 미등록 클래스의 구성과 크기에 변동이 생기는 인증 환경의 경우에, 강인한 성능을 보이기 어려웠다. 본 논문에서는 강인한 얼굴 등록자 인증을 위하여, 효과적인 클래스 구별 특징 벡터 기반 SVM을 제안한다. 훈련과 인증에 사용되는 특징 벡터는 2개의 클래스를 잘 구별할 수 있는 특성을 반영하도록 선택되었기 때문에 이를 이용하여 훈련된 제안된 SVM은 등록자 클래스 구성의 변화 및 얼굴 이미지에 있어서의 조명, 얼굴 자세, 얼굴 표정의 변화에 덜 영향을 받는다. 실험을 통해 제안된 SVM에 기반을 둔 얼굴 등록자 인증 방법이 기존 SVM에 기반을 둔 방법보다 성능이 더 나으며, 등록자 클래스 구성의 변화에도 강인함을 보였다.

Keywords

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