한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제17권3호
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  • Pages.403-409
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  • 2007
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  • 1976-9172(pISSN)
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  • 2288-2324(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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Contourlet 변환 및 PCA에 의한 얼굴인식

Face Recognition using Contourlet Transform and PCA

  • 송창규 (충북대학교 BK2l 충북정보기술사업단) ;
  • 권석영 (충북대학교 전기전자컴퓨터공학부) ;
  • 전명근 (충북대학교 전기전자컴퓨터공학부)
  • 발행 : 2007.06.30
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초록

컨투어렛 변환은 2차원의 웨이블렛 변환을 확장한 개념으로 다중스케일과 방향성필터뱅크를 이용한다. 이러한 컨투어렛 변환은 웨이블렛 변환의 특징인 다중스케일과 시간-주파수의 지역적 특성뿐만 아니라 방향성분에 대해서도 풍부한 정보를 얻을 수 있는 장점을 가지고 있다 본 논문에서는 컨투어렛 변환과 주성분분석기법을 이용하는 융합기법에 의한 얼굴인식 시스템을 제안한다. 제안된 방법은 먼저 컨투어렛 변환에 의해 얼굴영상을 방향성 부대역 영상으로 분할한 후, 주성분분석기법을 이용하여 방향성분별로 분할된 각각의 부영상에 대하여 특징벡터를 산출한다. 그리고 최종 단계에서는 각각의 대역별로 산출된 매칭도를 효과적으로 융합할 수 있는 융합기법을 이용하여 얼굴인식을 수행한다. 제안된 방법의 타당성을 보이기 위해 ORL 얼굴영상과 CBNU 얼굴영상을 대상으로 실험한 결과 기존 방법인 PCA나 웨이블렛 변환을 이용한 방법에 비해 향상된 인식 성능을 보임을 확인할 수 있었다.

Contourlet transform is an extention of the wavelet transform in two dimensions using the multiscale and directional fillet banks. The contourlet transform has the advantages of multiscale and time-frequency-localization properties of wavelets, but also provides a high degree of directionality. In this paper, we propose a face recognition system based on fusion methods using contourlet transform and PCA. After decomposing a face image into directional subband images by contourlet, features are obtained in each subband by PCA. Finally, face recognition is performed by fusion technique that effectively combines similarities calculated respectively In each local subband. To show the effectiveness of the proposed method, we performed experiments for ORL and CBNU dataset, and then we obtained better recognition performance in comparison with the results produced by conventional methods.

키워드

참고문헌

  1. Rodrigo de Luis-Garcia, Carlos Alberola-Lopez, Otman Aghzout and Juan Ruiz-Alzola 'Biometric identification systems,' Signal Processing, Vol. 83, Issue 12. pp. 2539-2557, December 2003 https://doi.org/10.1016/j.sigpro.2003.08.001
  2. M. Turk, A. Pentland, 'Eigenfaces for recognition,' Journal of Cognitive Neuro-science, Vol. 3, No.1, pp. 71-86, 1991 https://doi.org/10.1162/jocn.1991.3.1.71
  3. P. Belhumeur, J. Hespanha, D. Kriegman, 'Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,' IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19, No.7, pp. 711-720, 1997 https://doi.org/10.1109/34.598228
  4. Nefian, A.V, Hayes, M.H., III 'Face detection and recognition using hidden Markov models,' Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on, Vol. 1, 4-7 Oct 1998
  5. A- V.I.Rosti, M.J.F. Gales, 'Factor analysed hidden Markov models for speech recognition,' computer Speech & Language, Vol. 18, Issue 2, pp. 181-200, April 2004 https://doi.org/10.1016/j.csl.2003.09.004
  6. Nefian, A.V., Hayes, M.H., III 'Hidden Markov models for face recognition,' Acoustics, Speech, and Signal Processing, 1998. ICASSP '98. Proceedings of the 1998 IEEE International Conference on, Vol. 5, pp. 12-15, May 1998
  7. Kohir, V.V., Desai, UB. 'Face recognition using a DCT-HMM approach,' Applications of Computer Vision, 1998. WACV '98. Proceedings, Fourth IEEE Workshop on, pp. 19-21, Oct. 1998
  8. Keun-Chang Kwak and Pedrycz, W., 'Face recognition using fuzzy Integral and wavelet decomposition method,' IEEE Trans. on Systems, Man and Cybernetics, Part B, Vol. 34, Issue 4, pp. 1666-1675, Aug. 2004 https://doi.org/10.1109/TSMCB.2004.827609
  9. Duncan D.Y.Po, Minh N. Do, 'Directional Multiscale Modeling of Images using the Contourlet Trasnform', IEEE Trans. on Image Processing, Vol. 15, Issue 6, pp. 1610-1620, June 2006 https://doi.org/10.1109/TIP.2006.873450
  10. Minh N. Do, Martin Vetterli, 'The Contourlet Transform: An Efficient Directional Multiresolution Image Representation,' IEEE Trans. on Image Processing, Vol. 14, Issue 12, pp. 2091-2106, Dec. 2005 https://doi.org/10.1109/TIP.2005.859376
  11. P. J. Burt and E. H. Adelson, 'The Laplacian pyramid as a compact image code,' IEEE Trans. Commun., Vol. 31, No.4, pp. 532-540, Apr., 1983 https://doi.org/10.1109/TCOM.1983.1095851
  12. M. N. Do and M. Vetterli, 'Framing pyramids,' IEEE Trans. Signal Proc., pp. 2329-2342, Sep., 2003
  13. M. N. Do, Directional multiresolution image representations, Ph.D. dissertation, Swiss Federal Institute of Technology, Lausanne, Switzerland, December 2001, http://www.ifp.uiuc.edu/tminhdo/ publications
  14. M. Vetterli, 'Multidimensional subband coding: Some theory and algorithms,' Signal Proc., Vol.. 6, No.2, pp. 97-112, Feb., 1984 https://doi.org/10.1016/0165-1684(84)90012-4
  15. Han-Xiong Li and Shaocheng Tong, 'A Hybrid Adaptive Fuzzy Control for A Class of Nonlinear MIMO Systems,' IEEE Trans. on Fuzzy Systems, Vol. 11, No.1, pp. 24-34, 2003 https://doi.org/10.1109/TFUZZ.2002.806314
  16. Hazim Kernal Ekenel, Bulent Sankur, 'Multiresolution face recognition,' Image and Vision Computing, 2005