The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Improved $(2D)^2$ DLDA for Face Recognition

얼굴 인식을 위한 개선된 $(2D)^2$ DLDA 알고리즘

  • 조동욱 (충북과학대학 정보통신과학과) ;
  • 장언동 (충북대학교 정보통신공학과) ;
  • 김영길 (충북대학교 정보통신공학과) ;
  • 김관동 (충북대학교 정보통신공학과) ;
  • 안재형 (충북대학교 정보통신공학과) ;
  • 김봉현 (한밭대학교 정보통신전문대학원 컴퓨터공학과) ;
  • 이세환 (한밭대학교 정보통신전문대학원 컴퓨터공학과)
  • Published : 2006.10.31
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Abstract

In this paper, a new feature representation technique called Improved 2-directional 2-dimensional direct linear discriminant analysis (Improved $(2D)^2$ DLDA) is proposed. In the case of face recognition, thesmall sample size problem and need for many coefficients are often encountered. In order to solve these problems, the proposed method uses the direct LDA and 2-directional image scatter matrix. Moreover the selection method of feature vector and the method of similarity measure are proposed. The ORL face database is used to evaluate the performance of the proposed method. The experimental results show that the proposed method obtains better recognition rate and requires lesser memory than the direct LDA.

Keywords

References

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