Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Face Recognition Using Wavelet Coefficients and Hidden Markov Model

웨이블렛 계수와 Hidden Markov Model을 이용한 얼굴인식 기법

  • 이경아 (충북대학교 전기전자컴퓨터 공학부) ;
  • 이대종 (충북대학교 전기전자컴퓨터 공학부) ;
  • 박장환 (충주대학교 정보 제어 공학과) ;
  • 전명근 (충북대학교 전기전자컴퓨터 공학부)
  • Published : 2003.12.01
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Abstract

In this paper, we proposes a method for face recognition using HMM(hidden Markov model) and wavelet coefficients First, input images are compressed by using the multi-resolution analysis based on the discrete wavelet transform. And then, the wavelet coefficients obtained from each subband are used as feature vectors to construct the HMMs. In the recognition stage, we obtained higher recognition rate by summing of each recognition rate of wavelet subband. The usefulness of the proposed method was shown by comparing with conventional VQ and DCT-HMM ones. The experimental results show that the proposed method is more satisfactory than previous ones.

본 논문에서는 웨이블렛 계수와 Hidden Markov Model(HMM) 이용한 얼굴인식 알고리즘을 제안 한다. 입력 영상은 이산웨이블렛을 기반으로 한 다행상도 분석기법을 사용하여 데이터 수를 압축한 후, 각각의 해상도에서 얻어진 웨이블렛 계수를 특징벡터로 사용하여 HMM의 모델을 생성한다. 인식단계 에서는 웨이블렛 변환에 의해 생성된 개별대역의 인식값을 더하여 상호 보완함으로써 인식률을 높일 수 있었다. 제안된 알고리즘의 타당성을 검증하기 위하여 기본적 알고리즘인 벡터 양자화(VQ) 기법을 적용한 경우와 기존 얼굴인식에 제안된 DCT-HMM을 이용한 기법과의 인식률 비교를 한 결과, 제안된 방법이 우수한 성능을 보임을 알 수 있었다.

Keywords

References

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