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access icon openaccess Score calibration in face recognition

An evaluation of the verification and calibration performance of a face recognition system based on inter-session variability modelling is presented. As an extension to calibration through linear transformation of scores, categorical calibration is introduced as a way to include additional information about images for calibration. The cost of likelihood ratio, which is a well-known measure in the speaker recognition field, is used as a calibration performance metric. The results obtained from the challenging mobile biometrics and surveillance camera face databases indicate that linearly calibrated face recognition scores are less misleading in their likelihood ratio interpretation than uncalibrated scores. In addition, the categorical calibration experiments show that calibration can be used not only to improve the likelihood ratio interpretation of scores, but also to improve the verification performance of a face recognition system.

References

    1. 1)
      • 27. Križaj, J., Štruc, V., Pavešič, N.: ‘Adaptation of SIFT features for robust face recognition’. ICIAR10, 2010, pp. 394404.
    2. 2)
      • 26. Ahonen, T., Hadid, A., Pietikainen, M.: ‘Face recognition with local binary patterns’. European Conf. Computer Vision, Proc. Workshop on Dynamical Vision, 2004, pp. 469481.
    3. 3)
      • 51. Mandasari, M.I., Saeidi, R., McLaren, M., van Leeuwen, D.A.: ‘Quality measure functions for calibration of speaker recognition system in various duration conditions’, IEEE Trans. Audio Speech Lang. Process., 2013.
    4. 4)
      • 50. van Leeuwen, D.A., Brümmer, N.: ‘The distribution of calibrated likelihood-ratios in speaker recognition’, 2013, Interspeech.
    5. 5)
    6. 6)
    7. 7)
      • 10. Jafri, R., Arabnia, H.R.: ‘A survey of face recognition techniques’, JIPS, 2009, 5, (2), pp. 4168.
    8. 8)
    9. 9)
      • 23. Champod, C., Evett, I.W.: ‘A probabilistic approach to fingerprint evidence’, J. Forensic Identif., 2001, 51, (2), pp. 101122.
    10. 10)
    11. 11)
      • 32. Phillips, P.J.: ‘Support vector machines applied to face recognition’. Advances in Neural Information Processing Systems (MIT Press, 1999), vol. 11, pp. 803809.
    12. 12)
    13. 13)
      • 20. National Institute of Standards and Technology: The NIST Year 2010 Speaker Recognition Evaluation Plan. Available at: http://www.nist.gov/itl/iad/mig/sre12.cfm.
    14. 14)
    15. 15)
      • 42. Garcia-Romero, D., Fierrez-Aguilar, J., Gonzalez-Rodriguez, J., Ortega-Garcia, J.: ‘On the use of quality measures for text-independent speaker recognition’. Odyssey: The Speaker and Language Recognition Workshop. International Speech Communication Association (ISCA), 2004.
    16. 16)
    17. 17)
    18. 18)
    19. 19)
      • 36. Anjos, A., El Shafey, L., Wallace, R., Günther, M., McCool, C., Marcel, S.: ‘Bob: a free signal processing and machine learning toolbox for researchers’. 20th ACM Conf. Multimedia Systems (ACMMM), Nara, Japan, October 2012.
    20. 20)
      • 17. Ramos-Castro, D., Gonzalez-Rodriguez, J., Ortega-Garcia, J.: ‘Likelihood ratio calibration in a transparent and testable forensic speaker recognition framework’. Odyssey: The Speaker and Language Recognition Workshop, IEEE, International Speech Communication Association (ISCA), 2006, pp. 18.
    21. 21)
      • 1. Bolle, R.M., Connell, J.H., Pankanti, S., Ratha, N.K., Senior, A.W.: ‘The common biometrics: Guide to biometrics’ (Springer, 2004).
    22. 22)
    23. 23)
    24. 24)
      • 31. Moghaddam, B., Wahid, W., Pentland, A.: ‘Beyond eigenfaces: probabilistic matching for face recognition’. IEEE Int. Conf. Automatic Face and Gesture Recognition, 1998, pp. 3035.
    25. 25)
    26. 26)
      • 52. McCool, C., Marcel, S., Hadid, A., et al: ‘Bi-modal person recognition on a mobile phone: using mobile phone data’. IEEE ICME Workshop on Hot Topics in Mobile Multimedia, July 2012, pp. 635640.
    27. 27)
    28. 28)
      • 54. Lui, Y.M., Bolme, D.S., Phillips, P.J., Beveridge, J.R., Draper, B.A.: ‘Preliminary studies on the good, the bad, and the ugly face recognition challenge problem’. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2012, pp. 916.
    29. 29)
    30. 30)
      • 13. Pelecanos, J., Sridharan, S.: ‘Feature warping for robust speaker verification’. Odyssey: The Speaker and Language Recognition Workshop, International Speech Communication Association (ISCA), 2001, pp. 213218.
    31. 31)
      • 46. Günther, M., Costa-Pazo, A., Ding, C., et al: ‘The 2013 face recognition evaluation in mobile environment’. The Sixth IAPR Int. Conf. Biometrics, 2013.
    32. 32)
    33. 33)
      • 18. Castro, D.R.: ‘Forensic evaluation of the evidence using automatic speaker recognition systems’. PhD thesis, Universidad autónoma de Madrid, 2007.
    34. 34)
    35. 35)
    36. 36)
    37. 37)
      • 41. van Leeuwen, D.A.: ‘The TNO SRE-2008 speaker recognition system’. Proc. NIST Speaker Recognition Evaluation Workshop, Montreal, 2009.
    38. 38)
      • 47. van Leeuwen, D.A., Brümmer, N.: ‘An introduction to application-independent evaluation of speaker recognition systems’. Speaker classification I (Springer, 2007), pp. 330353.
    39. 39)
    40. 40)
      • 45. Martin, A., Doddington, G., Kamm, T., Ordowski, M., Przybocki, M.: ‘The DET curve in assessment of detection task performance’. Technical Report, DTIC Document, 1997.
    41. 41)
      • 35. Günther, M., Wallace, R., Marcel, S.: ‘An open source framework for standardized comparisons of face recognition algorithms’, in: Fusiello, A., Murino, V., Cucchiara, R. (eds.): Computer Vision – ECCV 2012. Workshops and Demonstrations, Volume 7585 of Lecture Notes in Computer Science, Berlin, October 2012, pp. 547556.
    42. 42)
      • 34. Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: ‘Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition’. IEEE Int. Conf. Computer Vision, 2005, vol. 1, pp. 786791.
    43. 43)
      • 30. Zhao, W., Krishnaswamy, A., Chellappa, R., Swets, D.L., Weng, J.: ‘Discriminant analysis of principal components for face recognition’ (Springer, Berlin, 1998), pp. 7385. http://www.face-rec.org/algorithms/LDA/zhao98discriminant.pdf.
    44. 44)
      • 48. Brümmer, N., de Villiers, E.: ‘The Bosaris toolkit: theory, algorithms and code for surviving the new DCF’. arXiv preprint arXiv:1304.2865, 2013.
    45. 45)
      • 22. Champod, I.C., Evett, I.W., Kuchler, B.: ‘Earmarks as evidence: a critical review’, J. Forensic Sci., 2001, 46, (6), pp. 1275.
    46. 46)
    47. 47)
      • 14. Wallace, R., McLaren, M., McCool, C., Marcel, S.: ‘Inter-session variability modelling and joint factor analysis for face authentication’. Int. Joint Conf. Biometrics (IJCB), 2011, pp. 18.
    48. 48)
      • 38. Zheng, R., Zhang, S., Xu, B.: ‘A comparative study of feature and score normalization for speaker verification’. Proc. 2006 Int. Conf. Advances in Biometrics, ICB'06, Berlin, Heidelberg, 2006, pp. 531538.
    49. 49)
      • 29. Cox, D.D., Pinto, N.: ‘Beyond simple features: a large-scale feature search approach to unconstrained face recognition’, 2011.
    50. 50)
      • 24. Poh, N., Tistarelli, M.: ‘Customizing biometric authentication systems via discriminative score calibration’. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2012, pp. 26812686.
    51. 51)
    52. 52)
    53. 53)
      • 55. Cardinaux, F., Sanderson, C., Marcel, S.: ‘Comparison of MLP and GMM classifiers for face verification on XM2VTS’. Fourth Int. Conf. Audio- and Video-Based Biometric Person Authentication, University of Surrey, Guildford, UK, 2003.
    54. 54)
    55. 55)
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