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Issue No.02 - February (2010 vol.32)
pp: 220-230
Neil Yager , Biometix, Eveleigh
Ted Dunstone , Biometix, Sydney
It is commonly accepted that users of a biometric system may have differing degrees of accuracy within the system. Some people may have trouble authenticating, while others may be particularly vulnerable to impersonation. Goats, wolves, and lambs are labels commonly applied to these problem users. These user types are defined in terms of verification performance when users are matched against themselves (goats) or when matched against others (lambs and wolves). The relationship between a user's genuine and impostor match results suggests four new user groups: worms, doves, chameleons, and phantoms. We establish formal definitions for these animals and a statistical test for their existence. A thorough investigation is conducted using a broad range of biometric modalities, including 2D and 3D faces, fingerprints, iris, speech, and keystroke dynamics. Patterns that emerge from the results expose novel, important, and encouraging insights into the nature of biometric match results. A new framework for the evaluation of biometric systems based on the biometric menagerie, as opposed to collective statistics, is proposed.
Biometrics, performance evaluation, authentication, identification, recognition, fingerprint, face, speech, iris, keystroke dynamics.
Neil Yager, Ted Dunstone, "The Biometric Menagerie", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 2, pp. 220-230, February 2010, doi:10.1109/TPAMI.2008.291
[1] T. Dunstone and N. Yager, Biometric System and Data Analysis: Design, Evaluation, and Data Mining. Springer, 2008.
[2] G. Doddington, W. Liggett, A. Martin, M. Przybocki, and D. Reynolds, “Sheep, Goats, Lambs and Wolves: A Statistical Analysis of Speaker Performance in the NIST 1998 Speaker Recognition Evaluation,” Proc. Int'l Conf. Spoken Language Processing, 1998.
[3] J.L. Wayman, “Multi-Finger Penetration Rate and ROC Variability for Automatic Fingerprint Identification Systems,” technical report, Nat'l Biometric Test Center, 1999.
[4] M. Wittman, P. Davis, and P. Flynn, “Empirical Studies of the Existence of the Biometric Menagerie in the FRGC 2.0 Color Image Corpus,” Proc. Computer Vision and Pattern Recognition Workshop, 2006.
[5] N. Poh and J. Kittler, “Incorporating Model-Specific Score Distribution in Speaker Verification Systems,” IEEE Trans. Audio, Speech, and Language Processing, vol. 16, no. 3, pp. 594-606, Mar. 2008.
[6] K. Chen, “Towards Better Making a Decision in Speaker Verification,” Pattern Recognition, vol. 36, no. 2, pp. 329-346, 2003.
[7] N. Poh, A. Ross, and S. Bengio, “Revisiting Doddington's Zoo: Employing User-Dependent Performance Criterion for Multibiometric Fusion,” Proc. Multimodal User Authentication Workshop, 2006.
[8] D. Ramos-Castro, J. Fierrez-Aguilar, J. Gonzalez-Rodriguez, and J. Ortega-Garcia, “Speaker Verification Using Speaker- and Test-Dependent Fast Score Normalization,” Pattern Recognition Letters, vol. 28, no. 1, pp. 90-98, 2007.
[9] R. Snelick, U. Uludag, A. Mink, M. Indovina, and A. Jain, “Large Scale Evaluation of Multimodal Biometric Authentication Using State-of-the-Art Systems,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 450-455, Mar. 2005.
[10] N. Yager and T. Dunstone, “Worms, Chameleons, Phantoms and Doves: New Additions to the Biometric Menagerie,” Proc. AutoID, 2007.
[11] W. Daniel, Applied Nonparametric Statistics. Wadsworth Publishing Company, 1989.
[12] R. Bolle, J. Connell, S. Pankanti, N. Ratha, and A. Senior, Guide to Biometrics. Springer-Verlag, 2003.
[13] “Performix Biometric Research and Analysis Software,” www. biometix.comperformix.htm, 2007.
[14] N. Yager and A. Amin, “Fingerprint Verification Using Two Stage Optimization,” Pattern Recognition Letters, vol. 27, pp. 317-324, 2006.
[15] D. Maio, D. Maltoni, R. Cappelli, J.L. Wayman, and A.K. Jain, “FVC2002: Second Fingerprint Verification Competition,” Proc. Int'l Conf. Pattern Recognition, vol. 3, pp. 811-814, 2002.
[16] N. Poh and S. Bengio, “Database, Protocol and Tools for Evaluating Score-Level Fusion Algorithms in Biometric Authentications,” Pattern Recognition, vol. 39, no. 2, pp. 223-233, 2005.
[17] P.J. Phillips, P.J. Flynn, T. Scruggs, K.W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, “Overview of the Face Recognition Grand Challenge,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
[18] J. Cook, V. Chandran, and C. Fookes, “3D Face Recognition Using Log-Gabor Templates,” Proc. British Machine Vision Conf., 2006.
[19] D. Gunetti and C. Picardi, “Keystroke Analysis of Free Text,” ACM Trans. Information and System Security, vol. 8, no. 3, pp. 312-347, 2005.
[20] A. Hicklin, C. Watson, and B. Ulery, “The Myth of Goats: How Many People Have Fingerprints That Are Hard to Match?” Technical Report NIST IR 7271, Nat'l Inst. of Standards and Tech nology, 2005.
[21] Authi-Corp, “IRIS06 Draft Final Report,” http://www. report/, 2007.
[22] D. Maltoni, D. Maio, A. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition. Springer, 2003.
[23] T.J. Atkinson and M.E. Schuckers, “Approximate Confidence Intervals for Estimation of Matching Error Rates of Biometric Identification Devices,” Proc. Biometric Authentication: European Conf. Computer Vision Int'l Workshop, 2004.
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