Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
권호 표지
DOI QR코드

DOI QR Code

Authentication Performance Optimization for Smart-phone based Multimodal Biometrics

스마트폰 환경의 인증 성능 최적화를 위한 다중 생체인식 융합 기법 연구

  • Moon, Hyeon-Joon (School of Computer Science and Engineering, Sejong University) ;
  • Lee, Min-Hyung (School of Mechanical and Aerospace Engineering, Sejong University) ;
  • Jeong, Kang-Hun (School of Computer Science and Engineering, Sejong University)
  • 문현준 (세종대학교 컴퓨터공학부) ;
  • 이민형 (세종대학교 기계항공우주공학부) ;
  • 정강훈 (세종대학교 컴퓨터공학부)
  • Received : 2015.04.06
  • Accepted : 2015.06.20
  • Published : 2015.06.28
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we have proposed personal multimodal biometric authentication system based on face detection, recognition and speaker verification for smart-phone environment. Proposed system detect the face with Modified Census Transform algorithm then find the eye position in the face by using gabor filter and k-means algorithm. Perform preprocessing on the detected face and eye position, then we recognize with Linear Discriminant Analysis algorithm. Afterward in speaker verification process, we extract the feature from the end point of the speech data and Mel Frequency Cepstral Coefficient. We verified the speaker through Dynamic Time Warping algorithm because the speech feature changes in real-time. The proposed multimodal biometric system is to fuse the face and speech feature (to optimize the internal operation by integer representation) for smart-phone based real-time face detection, recognition and speaker verification. As mentioned the multimodal biometric system could form the reliable system by estimating the reasonable performance.

본 논문에서는 스마트폰 환경의 얼굴 검출, 인식 및 화자 인증 기반 다중생체인식 개인인증 시스템을 제안한다. 제안된 시스템은 Modified Census Transform과 gabor filter 및 k-means 클러스터 분석 알고리즘을 통해 얼굴의 주요 특징을 추출하여 얼굴인식을 위한 데이터 전처리를 수행한다. 이후 Linear Discriminant Analysis기반 본인 인증을 수행하고(얼굴인식), Mel Frequency Cepstral Coefficient기반 실시간성 검증(화자인증)을 수행한다. 화자인증에 사용하는 음성 정보는 실시간으로 변화하므로 본 논문에서는 Dynamic Time Warping을 통해 이를 해결한다. 제안된 다중생체인식 시스템은 얼굴 및 음성 특징 정보를 융합 및 스마트폰 환경에 최적화하여 실시간 얼굴검출, 인식과 화자인증 과정을 수행하며 단일 생체인식에 비해 약간 낮은 95.1%의 인식률을 보이지만 1.8%의 False Acceptance Ratio를 통해 객관적인 실시간 생체인식 성능을 입증하여 보다 신뢰할 수 있는 시스템을 완성한다.

Keywords

References

  1. A. K. Jain, "Biometrics Personal Identification in Networked Society," Kleuwer Academic Publishers, 1999.
  2. Z. Akhtar, N. Alfarid, "Robustness of Serial and Parallel Biometric Fusion against Spoof Attacks", Communications in Computer and Information Science, Volume 157, pp 217-225, 2011. https://doi.org/10.1007/978-3-642-22786-8_27
  3. A. Tolba, A. El-Baz, A. El-Harby, "Face Recognition: A Literature Review," Intern. Journ. of Signal Processing, Vol. 2, No. 2, pp. 88-103, 2006.
  4. W. Zhao, "Face Recognition: A Literature Survey," ACM Computing Surveys, Vol. 35, No. 4, pp. 339-458, 2003.
  5. K. Jeong, S. Kim, and H. Moon, "Enhancement of Mobile Authentication System Performance based on Multimodal Biometrics," The 39th KIPS Spring Conference 2013.
  6. S. Xie, S. Shan, X. Chen, J. Chen, "Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition," IEEE Trans. on Image Processing, Vol. 19, No. 5, pp. 1349-1361, May 2010. https://doi.org/10.1109/TIP.2010.2041397
  7. Z. Qian, D. Xu, "Automatic Eye Detection Using Intensity Filtering and K-means Clustering," Pattern Recognition Letters, Volume 31, Issue 12, Pages 1633-1640, 1 September 2010. https://doi.org/10.1016/j.patrec.2010.05.012
  8. M. H. Yang, D. Kriegman, N. Ahuja, "Detecting faces in images: A survey," IEEE Transactions on Pattern Analysis and machine Intelligence, 24(1) : 34-58, January 2002. https://doi.org/10.1109/34.982883
  9. J. Ruiz-del-Solar, P. Navarrete, "Eigenspace- Based Face Recognition: A Comparative Study of Different Approaches," IEEE Trans. Systems, Man, and Cybernetics- Part C 35(3), 315-325, 2005. https://doi.org/10.1109/TSMCC.2005.848201
  10. W. Shen, R. Khanna, "Prolog To Speaker Recognition: A Tutorial," Proceedings of the IEEE, vol.85, no.9, pp.1436, Sept. 1997. https://doi.org/10.1109/JPROC.1997.628713
  11. M. R. Hasan, M. Jamil, M. G. Rabbani, & M. S. Rahman, "Speaker identification using Mel frequency cepstral coefficients. variations," 3rd International Conference on Electrical & Computer Engineering ICECE 2004
  12. D. J. Berndt, J. Clifford, "Using Dynamic Time Warping to Find Patterns in Time Series," In KDD workshop, Vol. 10, No. 16, pp. 359-370, 1994.
  13. D. A. Reynolds, "An overview of automatic speaker recognition technology," Proceedings of the International Conference on Acoustics, Speech, and Signal Processing(ICASSP)(S. 4072-4075). 2002
  14. E. Keogh, C. A. Ratanamahatana, "Exact indexing of dynamic time warping," Knowledge and Information Systems, Volume 7, Issue 3, pp 358-386, March 2005. https://doi.org/10.1007/s10115-004-0154-9
  15. H. Moon, P. J. Phillips, "Computational and Performance Aspects of PCA-Based Face-Recognition Algorithms. Perception," Vol. 30. pp. 303-321, 2001. https://doi.org/10.1068/p2896

Cited by

  1. User Authentication Algorithm Guaranteeing Reusability of Biometric Data in BioPKI System vol.93, pp.1, 2017, https://doi.org/10.1007/s11277-016-3595-z