DOI QR코드

DOI QR Code

Enhancement of Authentication Performance based on Multimodal Biometrics for Android Platform

안드로이드 환경의 다중생체인식 기술을 응용한 인증 성능 개선 연구

  • 최성필 (세종대학교 컴퓨터 공학과) ;
  • 정강훈 (세종대학교 컴퓨터 공학과) ;
  • 문현준 (세종대학교 컴퓨터 공학과)
  • Received : 2012.12.03
  • Accepted : 2013.01.17
  • Published : 2013.03.31

Abstract

In this research, we have explored personal authentication system through multimodal biometrics for mobile computing environment. We have selected face and speaker recognition for the implementation of multimodal biometrics system. For face recognition part, we detect the face with Modified Census Transform (MCT). Detected face is pre-processed through eye detection module based on k-means algorithm. Then we recognize the face with Principal Component Analysis (PCA) algorithm. For speaker recognition part, we extract features using the end-point of voice and the Mel Frequency Cepstral Coefficient (MFCC). Then we verify the speaker through Dynamic Time Warping (DTW) algorithm. Our proposed multimodal biometrics system shows improved verification rate through combining two different biometrics described above. We implement our proposed system based on Android environment using Galaxy S hoppin. Proposed system presents reduced false acceptance ratio (FAR) of 1.8% which shows improvement from single biometrics system using the face and the voice (presents 4.6% and 6.7% respectively).

본 논문은 모바일 환경에서의 다중생체인식을 통한 개인인증 시나리오에서 false acceptance rate (FAR)가 향상된 시스템을 제안한다. 다중생체인식을 위하여 얼굴인식과 화자인식을 선택하였으며, 시스템의 인식 시나리오는 다음을 따른다. 얼굴인식을 위하여 Modified census transform (MCT) 기반의 얼굴검출과 k-means 클러스터 분석 (cluster analysis) 알고리즘 기반의 눈 검출을 통해 얼굴영역 전처리를 수행하고, principal component analysis (PCA) 기반의 얼굴인증 시스템을 구현한다. 화자인식을 위하여 음성의 끝점추출과 Mel frequency cepstral coefficient (MFCC) 특징을 추출하고, dynamic time warping (DTW) 기반의 화자 인증 시스템을 구현한다. 그리고 각각의 생체인식을 본 논문에서 제안된 방법을 기반으로 융합하여 인식률을 향상시킨다. 본 논문의 실험은 Android 환경에서 수행하였으며, 구현한 다중생체인식 시스템과 단일생체인식 시스템과의 FAR을 비교하였다. 단일 얼굴인식의 FAR은 4.6%, 단일 화자인식의 FAR은 6.7%로 각각 나타났으며, 제안된 다중생체인식 시스템의 FAR은 1.8%로 크게 감소하였다.

Keywords

References

  1. A. K. Jain, Biometrics Personal Identification in Networked Society, Kluwer Academic Publishers, Springer, 1999.
  2. D. Polemi, "Biometric Techniques : Review and Evaluation of Biometric Techniques for Identification and Authentication, Including an Appraisal of the Areas where They are Most Applicable," Reported prepared for the European Commision DG XIIIC, Vol. 4, 1997
  3. L. Hong, and A. K. Jain. "Integrating faces and fingerprints for personal identification." Pattern Analysis and Machine Intelligence, IEEE Transactions on. Vol. 20, Issue 12, pp. 1295-1307, 1998 https://doi.org/10.1109/34.735803
  4. R.N. Rodrigues, L.L. Ling, and V. Govindaraju, "Robustness of Multimodal Biometric Fusion Methods Against Spoof Attacks," Journal of Visual Languages & amp; Computing, Vol. 20, Issue 3, pp. 169-179, 2009. https://doi.org/10.1016/j.jvlc.2009.01.010
  5. B. Froba and A. Ernst, "Face Detection with the Modified Census Transform," Proc. IEEE Conf. Automatic Face and Gesture Recognition, pp. 91-96, 2004.
  6. R. Zabih and J. Woodfill, "Non-Parametric Local Transform for Computing Visual Correspondence," Proc. Euro-pean Conf. Computer Vision, Vol. 2, pp. 151-158, 1994.
  7. Z. Qian and D. Xu, "Automatic Eye Detection Using Intensity Filtering and K-means Clustering," Pattern Recognition Letters, Vol. 31, Issue 12, pp. 1633-1640, 2010. https://doi.org/10.1016/j.patrec.2010.05.012
  8. M. Turk and A. Pentland, "Eigenfaces for Recognition," Journal of Cognitive Neuroscience, Vol. 3, No. 1, pp. 71-86, 1991. https://doi.org/10.1162/jocn.1991.3.1.71
  9. J. Ruiz-del-Solar and P. Navarrete, "Eigenspace- Based Face Recognition: A Comparative Study of Different Approaches," IEEE Trans. Systems, Man, and Cybernetics, Part C, Vol. 35, No. 3, pp. 315-325, 2005. https://doi.org/10.1109/TSMCC.2005.848201
  10. W. Zhao, A. Krishnaswamy, R. Chellappa, D. L. Swets, and J. Weng, "Discriminant Analysis of Principal Components for Face Recognition," Face Recognition: From Theory to Applications, Vol. 163, No. 1, pp. 73-85, 1998.
  11. D.A. Reynolds, "An Overview of Automatic Speaker Recognition Technology," IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 4, pp. IV-4072-IV-4075, 2002.
  12. J.G. Wilpon, L.R. Rabiner, and T. Martin, "An Improved Word-Detection Algorithm for Telephone- Quality Speech Incorporating Both Syntactic and Semantic Constraints," AT&T Bell Labs. Tech. J., Vol. 63, No. 3, pp. 479-498, 1984. https://doi.org/10.1002/j.1538-7305.1984.tb00016.x
  13. L.R. Rabiner and M.R. Sambur, "An Algorithm for Determining the Endpoints of Isolated Utterances," Bell Syst. Tech. J., Vol. 54, No. 2, pp. 297-315, 1975. https://doi.org/10.1002/j.1538-7305.1975.tb02840.x
  14. J. C. Junqua, B. Reaves, and B. Mak, "A Study of Endpoint Detection Algorithms in Adverse Conditions: Incidence on a DTW and HMM Recognize," Proc. Eurospeech, pp. 1371-1374, 1991.
  15. R. Vergin, D. O'Shaughnessy, and A. Farhat, "Generalized Mel Frequency Cepstral Coefficients for Large-Vocabulary Speaker-Independent Continuous-Speech Recognition," IEEE Transactions on Speech and Audio Processing, Vol. 7, No. 5, pp. 525-532, 1999. https://doi.org/10.1109/89.784104
  16. C. Myers, L. Rabiner, and A. Rosenberg, "Performance Tradeoffs in Dynamic Time Warping Algorithms for Isolated Word Recognition," IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. 28, No. 6, pp. 623- 635, 1980. https://doi.org/10.1109/TASSP.1980.1163491
  17. E. Keogh and C. A. Ratanamahatana, "Exact Indexing of Dynamic Time Warping," Knowledge and Information Systems, Vol. 7, Issue 3, pp. 358-386, 2005. https://doi.org/10.1007/s10115-004-0154-9
  18. 밧수리수브다, 고재필, "얼굴인식을 위한 거리 척도학습 방법 비교," 멀티미디어학회논문지, 제14권, 제6호, pp. 711-718, 2011.
  19. E. Parzen, "On Estimation of a Probability Density Function and Mode," The Annals of Mathematical Statistics , Vol. 33, No. 3, pp. 1065-1076, 1962. https://doi.org/10.1214/aoms/1177704472

Cited by

  1. Virtual Keyboard against Social Engineering Attacks in Smartphones vol.18, pp.3, 2015, https://doi.org/10.9717/kmms.2015.18.3.368