KSII Transactions on Internet and Information Systems (TIIS)

  • 제11권12호
  • /
  • Pages.6133-6151
  • /
  • 2017
  • /
  • 1976-7277(pISSN)
  • /
  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
권호 표지
DOI QR코드

DOI QR Code

Periocular Recognition Using uMLBP and Attribute Features

  • 투고 : 2017.03.03
  • 심사 : 2017.08.09
  • 발행 : 2017.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The field of periocular biometrics has gained wide attention as an alternative or supplemental means to conventional biometric traits such as the iris or the face. Periocular biometrics provide intermediate resolution between the iris and the face, which enables it to support both. We have developed a periocular recognition system by using uniform Multiscale Local Binary Pattern (uMLBP) and attribute features. The proposed system has been evaluated in terms of major factors that need to be considered on a mobile platform (e.g., distance and facial pose) to assess the feasibility of the use of periocular biometrics on mobile devices. Experimental results showed 98.7% of rank-1 identification accuracy on a subset of the Face Recognition Grand Challenge (FRGC) database, which is the best performance among similar studies.

키워드

참고문헌

  1. J. Adams, D. L. Woodard, G. Dozier, P. Miller, K. Bryant, and G. Glenn, "Genetic-based type ii feature extraction for periocular biometric recognition: Less is more," in Proc. of Int. Conf. on Pattern Recognition, pp. 205-208, 2010.
  2. S. Bakshi, P. K. Sa, and B. Majhi, "A novel phase-intensive local pattern for periocular recognition under visible spectrum," Biocybernetics and Biomedical Engineering, vol. 35, no. 1, pp. 30-44, 2015. https://doi.org/10.1016/j.bbe.2014.05.003
  3. P. N. Belhumeur, J. P. Hespanha, and D. Kriegman, "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, 1997. https://doi.org/10.1109/34.598228
  4. S. Bharadwaj, H. Bhatt, M. Vatsa, and R. Singh, "Periocular biometrics: When iris recognition fails," in Proc. of IEEE Int. Conf. on Biometrics: Theory, Applications, and Systems, pp. 1-6, 2010.
  5. G. Dozier, K. Purrington, K. Popplewell, J. Shelton, T. Abegaz, K. Bryant, J. Adams, D. L. Woodard, and P. Miller. 2011. "GEFeS: Genetic & evolutionary feature selection for periocular biometric recognition," in Proc. of IEEE Workshop on Computational Intelligence in Biometrics and Identity Management, pp. 152-156, 2011.
  6. A. K. Jain, A. Ross, and S. Prabhakar, "An introduction to biometric recognition," IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 4-20, 2004. https://doi.org/10.1109/TCSVT.2004.839484
  7. S. M. Jonathon, and B. V. K. V. Kumar, "What is a "good" periocular region for recognition?" in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition Workshops, pp. 117-124, 2013.
  8. A. Joshi, A. K. Gangwar, and Z. Saquib, "Person recognition based on fusion of iris and periocular biometrics," in Proc. of Int. Conf. on Hybrid Intelligent Systems, pp. 57-62, 2012.
  9. F. Juefei-Xu, K. Luu, M. Savvides, T. Bui, and C. Suen. 2011. "Investigating age invariant face recognition based on periocular biometrics," in Proc. of Int. Joint Conf. on Biometrics, pp. 1-7, 2011.
  10. F. Juefei-Xu and M. Savvides, "Unconstrained periocular biometric acquisition and recognition using COTS PTZ camera for uncooperative and non-cooperative subjects," in Proc. of IEEE Workshop on Applications of Computer Vision, pp. 201-208, 2012.
  11. N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar, "Attribute and Simile Classifiers for Face Verification," IEEE Trans. on Pattern Analysis and Machine Intelligence, pp. 1962-1977, 2011.
  12. S. Liao, and S. Z. Li, "Learning multi-scale block local binary patterns for face recognition," in Proc. of Int. Conf. on Biometrics, pp. 828-837, 2007.
  13. S. Milborrow and F. Nicolls, "Active Shape Models with SIFT Descriptors and MARS," in Proc. of Int. Conf. on Computer Vision Theory and Application, pp. 380-387, 2014.
  14. O. Nikisins, "Weighted multi-scale local binary pattern histograms for face recognition," in Proc. of Int. Conf. on Applied Mathematics and Computational Methods, pp. 76-81, 2013.
  15. T. Ojala, M. Pietikainen, and D. Harwood, "A comparative study of texture measures with classification based on featured distributions," Pattern Recognition, vol. 29, no. 1, pp. 51-59, 1996. https://doi.org/10.1016/0031-3203(95)00067-4
  16. U. Park, R. Jillela, A. Ross, and A. K. Jain, "Periocular biometrics in the visible spectrum," IEEE Transactions on Information Forensics and Security, vol. 6, no. 1, pp. 96-106, 2011. https://doi.org/10.1109/TIFS.2010.2096810
  17. U. Park, A. Ross, and A. K. Jain, "Periocular biometrics in the visible spectrum: a feasibility study," in Proc. of IEEE Int. Conf. on Biometrics: Theory, Applications, and Systems, pp. 1-6, 2009.
  18. G. Santos, E. Grancho, M. V. Bernardo, and P. T. Fiadeiro, "Fusing Iris and Periocular Information for Cross-sensor recognition," Pattern Recognition Letters, vol. 57, pp. 52-59, 2015. https://doi.org/10.1016/j.patrec.2014.09.012
  19. W. R. Schwartz, A. Kembhavi, D. Harwood, and L. S. Davis, "Human Detection Using Partial Least Squares Analysis," in Proc. of Int. Conf. on Computer Vision, pp. 24-31, 2009.
  20. J. L. Starck, F. Murtagh, and A. Bijaoui, "Image restoration with noise suppression using the wavelet transform," Astronomy and Astrophysics, vol. 288, no. 1, pp. 342-348, 1994.
  21. M. Uzair, A. Mahmood, A. Mian, and C. McDonald, "Periocular biometric recognition using image sets," in Proc. of IEEE Workshop on Applications of Computer Vision, pp. 246-251, 2013.
  22. A. Vedaldi, and B. Fulkerson, "{VLFeat}: An Open and Portable Library of Computer Vision Algorithms." VLFeat Library. http://www.vlfeat.org/.
  23. D. L. Woodard, S. Pundlik, J. Lyle, and P. Miller, "Periocular region appearance cues for biometric identification," in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition Workshops, pp. 162-169, 2010.
  24. D. L. Woodard, S. Pundlik, P. Miller, R. Jillela, and A. Ross, "On the fusion of periocular and iris biometrics in non-ideal imagery," in Proc. of Int. Conf. on Pattern Recognition, pp. 201-204, 2010.
  25. L. Dumbgen, "On Tyler's M-functional of scatter in high dimension," Annals of the Institute of Statistical Mathematics, vol. 50, pp. 471-491, 1998. https://doi.org/10.1023/A:1003573311481
  26. E. Ollila, T. P. Hettmansperger, and H. Oja, "Affine equivariant multivariate sign methods," Preprint, University of Jyvaskyla, 2004.
  27. D. E. Tyler, "Robustness and efficiency properties of scatter matrices," Biometrika, vol. 70, pp. 411-420, 1983. https://doi.org/10.1093/biomet/70.2.411
  28. S. Bakshi, P. K. Sa, and B. Majhi, "Phase Intensive Global Pattern for Periocular Recognition," in Proc. of IEEE India Conference (INDICON), pp. 1-5, 2014.
  29. T. D. Bie, N. Cristianini, and R. Rosipal, "Eigenproblems in Pattern Recognition," Applications in Pattern Recognition, Computer Vision, Neuralcomputing, and Robotics, Ch. 5, part II, pp. 129-167, 2005, Springer Berlin Heidelberg.

피인용 문헌

  1. Face Spoofing Attack Detection Using Spatial Frequency and Gradient-Based Descriptor vol.13, pp.2, 2017, https://doi.org/10.3837/tiis.2019.02.022