대한전자공학회논문지SP (Journal of the Institute of Electronics Engineers of Korea SP)

대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지

영역기반 주성분 분석 방법과 보조정보를 이용한 얼굴정보의 비트열 변환 방법

A Study on A Biometric Bits Extraction Method Using Subpattern-based PCA and A Helper Data

  • 이형구 (연세대학교, 생체인식연구센터) ;
  • 정호기 (연세대학교, 생체인식연구센터)
  • Lee, Hyung-Gu (Yonsei University, Biometric Engineering Research Center) ;
  • Jung, Ho-Gi (Yonsei University, Biometric Engineering Research Center)
  • 투고 : 2010.02.17
  • 발행 : 2010.09.25
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초록

생체인식은 개인의 유일하면서 변화하지 않는 생체의 특징을 이용하여 개인의 본인 여부를 판별하는 방법으로써 널리 사용되어 왔다. 생체정보의 고유 불변한 특징을 저장하는 것은 개인정보의 노출에 따른 보안상의 문제점을 갖고 있으며 이를 해결하기 위해 제안된 방법이 가변생체인식 (cancelable biometrics)이다. 가변생체인식은 생체정보의 도난이나 도용으로부터 강인하며 재생성 가능한 생체템플릿을 제공하는 생체 인식방법이다. 본 논문에서는 변환 생체인식의 한 가지 방법으로써 얼굴 생체정보의 새로운 이진화 방법을 제안한다. 얼굴 생체정보의 이진화를 위한 특징추출은 얼굴정보의 부분적 변화에 강인한 영역기반 주성분 분석(Subpattern-based PCA)을 이용하였으며 이로부터 얻어진 특징을 보조정보에 기반한 방법으로 이진화 하였다. 획득된 이진비트열은 영역기반 주성분 분석의 사용으로 여러 얼굴 영역의 고려와 함께, 선택된 주성분 개수만큼의 계수들에 대한 이진화 값들을 포함하고 있다. 이러한 서로 다른 얼굴영역의 여러 주성분들에서 추출된 이진비트열중 구분력이 좋은 비트 값들을 선택하였으며, 선택된 비트 값들은 이진화를 위한 보조 정보가 노출된 경우에서도 원 얼굴특징벡터보다 향상된 인식성능을 보여준다.

Unique and invariant biometric characteristics have been used for secure user authentication. Storing original biometric data is not acceptable due to privacy and security concerns of biometric technology. In order to enhance the security of the biometric data, the cancelable biometrics was introduced. Using revocable and non-invertible transformation, the cancelable biometrics can provide a way of more secure biometric authentication. In this paper, we present a new cancelable bits extraction method for the facial data. For the feature extraction, the Subpattern-based Principle Component Analysis (PCA) is adopted. The Subpattern-based PCA divides a whole image into a set of partitioned subpatterns and extracts principle components from each subpattern area. The feature extracted by using Subpattern-based PCA is discretized with a helper data based method. The elements of the obtained bits are evaluated and ordered according to a measure based on the fisher criterion. Finally, the most discriminative bits are chosen as the biometric bits string and used for authentication of each identity. Even if the generated bits string is compromised, new bits string can be generated simply by changing the helper data. Because, the helper data utilizes partial information of the feature, the proposed method does not reveal privacy sensitive biometric information of the user. For a security evaluation of the proposed method, a scenario in which the helper is compromised by an adversary is also considered.

키워드

참고문헌

  1. N. K. Ratha, J. H. Connell, and R. M. Bolle, "Enhancing security and privacy in biometricsbased authentication systems," IBM systems Journal, vol. 40, pp. 614-634, 2001. https://doi.org/10.1147/sj.403.0614
  2. S. Chen and Y. Zhu, "Subpattern-based Principle component analysis," Pattern Recognition, vol 37, no 5, pp 1081-1083, 2004 https://doi.org/10.1016/j.patcog.2003.09.004
  3. Jean-paul Linnartz and Pim Tuyls, "New Shielding Functions to Enhance Privacy and Prevent Misuse of Biometric Templates," AVBPA, pp. 393-402, 2003.
  4. C. Vielhauer and R. Steinmetz, "Handwriting: feature correlation analysis for biometric hashes," EURASIP Journal on Applied Signal Processing, vol. 2004, no. 4, pp. 542-558, special issue on Biometric Signal Processing, 2004. https://doi.org/10.1155/S1110865704309248
  5. Qi Han, Zhifang Wang, and Xiamu Niu, "A Non-uniform Quantizing Approach to Protect Biometric Templates," International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP '06., pp. 693-698, 2006.
  6. Chen C., Veldhuis R.N.J., Kevenaar T.A.M., and Akkermans A.H.M., "Multi-Bits Biometric String Generation based on the Likelihood Ratio," First IEEE International Conference on Biometrics: Theory, Applications, and Systems, 2007. BTAS 2007., 2007.
  7. Chen C., Veldhuis R.N.J., Kevenaar T.A.M., and Akkermans A.H.M., "Biometric Quantization through Detection Rate Optimized Bit Allocation," EURASIP Journal on Advances in Signal Processing, vol. 2009, 2009.
  8. Pim Tuyls, Anton H. M. Akkermans, Tom A. M. Kevenaar, Geert Jan Schrijen, Asker M. Bazen, and Raymond N. J. Veldhuis, "Practical Biometric Authentication with Template Protection," International conference on: Audio- and Video-Based Biometric Person Authentication. AVBPA, Vol. 3546, pp. 436-446, 2005.
  9. Andrew Teoh, David Ngo and Alwyn Goh, "Biohashing: Two Factor Authentication Featuring Fingerprint Data And Tokenised Random Number," Pattern Recognition, Vol. 37, Issue 11, pp. 2245-2255, 2004. https://doi.org/10.1016/j.patcog.2004.04.011
  10. Andrew B. J. Teoh, Yip Wai Kuan, and Sangyoun Lee, "Cancellable biometrics and annotations on BioHash," Pattern Recognition, Vol. 41 , Issue 6, pp. 2034-2044, 2008. https://doi.org/10.1016/j.patcog.2007.12.002
  11. Andrew B.J. Teoh, Alwyn Goh, and David C.L. Ngo, "Random Multispace Quantization as an Analytic Mechanism for BioHashing of Biometric and Random Identity Inputs," IEEE transactions on a Pattern Analysis and Machine Intelligence, vol. 28, no. 12, 2006.
  12. Brian Chen and Gregory W. Wornell, " Quantization Index Modulation: A Class of Provably Good Methods for Digital Watermarking and Information Embedding," IEEE Trans. on Information Theory, vol. 47, no. 4, pp. 1423-1443, 2001. https://doi.org/10.1109/18.923725
  13. Anil K. Jain, Karthik Nandakumar, and Abhishek Nagar, "Biometric template security," EURASIP Journal on Advances in Signal Processing, vol. 2008, no. 113, 2008.
  14. T. Sim, S. Baker, and M. Bsat, "The CMU Pose, Illumination, and Expression Database," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, 2003.
  15. J. Daugman, "The Importance of Being Random: Statistical Principles of Iris Recognition," Pattern Recognition, vol. 36, no. 2, pp. 279-291, 2003. https://doi.org/10.1016/S0031-3203(02)00030-4