DOI QR코드

DOI QR Code

명암도 동시발생 행렬과 웨이블릿 특징 조합에 기반한 지문 분류 방법

A Fingerprint Classification Method Based on the Combination of Gray Level Co-Occurrence Matrix and Wavelet Features

  • 강승호 (국가수리과학연구소 수리생물학연구팀)
  • 투고 : 2013.03.18
  • 심사 : 2013.06.18
  • 발행 : 2013.07.31

초록

본 논문에서는 생체인증 시스템의 하나인 지문인식 시스템의 정확도와 효율성을 높이기 위한 새로운 지문 분류 방법을 제안한다. 기존 연구에 따르면 지문은 융선과 골의 방향과 형상에 따라 몇 가지 유형으로 분류할 수 있다. 지문 데이터베이스를 사전에 유형에 따라 분류해 놓고 인식 대상인 지문의 유형을 정확하게 분류할 수 있다면 지문 인식 시간을 크게 줄일 수 있다. 왜냐하면 선택된 부류 안의 지문들만을 상대로 인증 대상인 지문과 비교하면 되기 때문이다. 본 논문은 우선 지문 영상으로부터 실제 지문 정보가 위치하는 관심영역 추출 방법을 제시한다. 다음엔 추출된 관심영역을 대상으로 질감 인식기반의 명암도 동시발생 행렬과 웨이브릿 변환을 통한 특징 추출 방법을 제시하고 기존의 명암도 동시발생 행렬만을 이용한 특징 추출 방법과 다층 퍼셉트론 및 서포트 벡터 머신을 사용해 성능을 비교한다.

In this paper, we propose a novel fingerprint classification method to enhance the accuracy and efficiency of the fingerprint identification system, one of biometrics systems. According to the previous researches, fingerprints can be categorized into the several patterns based on their pattern of ridges and valleys. After construction of fingerprint database based on their patters, fingerprint classification approach can help to accelerate the fingerprint recognition. The reason is that classification methods reduce the size of the search space to the fingerprints of the same category before matching. First, we suggest a method to extract region of interest (ROI) which have real information about fingerprint from the image. And then we propose a feature extraction method which combines gray level co-occurrence matrix (GLCM) and wavelet features. Finally, we compare the performance of our proposed method with the existing method which use only GLCM as the feature of fingerprint by using the multi-layer perceptron and support vector machine.

키워드

참고문헌

  1. N. Yager and A. Amin, "Fingerprint Classification: A Review," Pattern Analysis and Applications, Vol. 7, No. 1, pp. 77-93, 2004. https://doi.org/10.1007/s10044-004-0204-7
  2. 강병준, 박강령, 유장희, 문기영, 김정녀, 신재호, "LDP 기반 비접촉식 지문 인식," 멀티미디어학회논문지, 제13권, 제9호, pp. 1337- 1347, 2010
  3. A.K. Jain, "Hierarchical Kernel Fitting for Fingerprint Classification and Alignment," Proc. International Conference on Pattern Recognition, Vol. 2, pp. 469-473, 2002.
  4. A.K. Jain, S. Prabhakar, and L. Hong, "A Multichannel Approach to Fingerprint Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 4, pp. 348-359, 1999. https://doi.org/10.1109/34.761265
  5. J.H. Chang and K.C. Fan, "A New Model for Fingerprint Classification by Ridge Distribution Sequences," Pattern Recognition, Vol. 35, pp. 1209-1223, 2002. https://doi.org/10.1016/S0031-3203(01)00121-2
  6. M. Yazdi and K. Gheysari, "A New Approach for the Fingerprint Classification Based on Gray-Lavel Co-Occurrence Matrix," International Journal of Computer and Information Engineering, Vol. 2, No. 7, pp. 456-459, 2008.
  7. F. Galton, Finger Prints, McMillan, London, 1892.
  8. E. Henry, Classification and Uses of Finger Prints, Rutledge, London, 1900.
  9. H. Tamura, S. Mori, and T. Yamawaki, "Texture Features Corresponding to Visual Perception," IEEE Transaction on Systems, Man and Cybernetics, Vol. 8, No. 6, pp. 123- 135, 1993.
  10. C.J. Lee, T.N. Yang, C.J. Chen, A.Y. Chang, and S.H. Hsu, "Fingerprint Identification using Local Gabor Filters," Proc. Networked Computing and Advanced Information Management, pp. 626-631, 2010.
  11. K.K. Benazir and Vijayakumar, "Fingerprint Matching by Extracting GLCM Features," Proc. International Conference & Workshop on Recent Trends in Technology, pp. 30-34, 2012.
  12. K. Kim, S. Jeong, B.T. Chun, J.Y. Lee, and Y. Bae, "Efficient Video Images Retrieval by Using Local Co-occurrence Matrix Texture Features and Normalised Correlation," Proc. the IEEE Region 10 Conference, pp. 934-937, 1999.
  13. D. Hazra, "Texture Recognition with combined GLCM, Wavelet and Rotated Wavelet Features," International J ournal of Computer and Electrical Engineering, Vol. 3, No. 1, pp. 146-150, 2011.
  14. R.M. Haralick, K. Shanmugan and J. Dinstein, "Textual Feature for Image Classification," IEEE transactions on Systems, Man and Cybernetics, Vol. SMC-3, No. 6, pp. 610-621, 1973. https://doi.org/10.1109/TSMC.1973.4309314
  15. 유훈, "디지털 영상 통신 시스템에서 웨이블릿 변환 기반 저역 필터와 보간 필터," 멀티미디어학회논문지, 제9권, 제4호, pp. 443-450, 2006.
  16. J.C. Platt, Sequential Minimal Optimization: a Fast Algorithm for Training Support Vector machine, Technical Report MSR-TR-98-14, 1998.
  17. T. Hastie and R. Tibshirani, "Classification by Pairwise Coupling," The Annals of Statistics, Vol. 26, No. 2, pp. 451-471, 1998. https://doi.org/10.1214/aos/1028144844
  18. http://bias.csr.unibo.it/fvc2000/download.asp, FVC 2000, 2000.
  19. http://bias.csr.unibo.it/fvc2002/download.asp, FVC 2002, 2002.
  20. http://bias.csr.unibo.it/fvc2004/download.asp, FVC 2004, 2004.
  21. http://www.neurotechnologija.com/download.html, VeriFinger_Sample_DB, 2007.