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Figure 23.9. Average D(p q) in bits (y axis) as a function of the mean feature variance (arbitrary
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units) (x axis) for 16 different persons. The mean feature variance is computed by summing all the diagonal components of Sp matrix for each person. The correlation coef cient is 0.62, which is signi cant at p < 0.01.
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r Inherent limits to biometric template size requirements. A maximum compression of biometric features will be limited to the biometric feature information. This theoretical lower limit may be of use for ID card applications with limited data density. r Feasibility of biometric encryption. Proposed biometric encryption systems use biometric data to generate keys [23], and thus the availability of biometric feature information limits the security of cryptographic key generation [24, 25]. r Performance limits of biometric matchers. While some algorithms outperform others, it clear that there are ultimate limits to error rates, based on the information available in the biometric features. In this application, the biometric feature information is related to the discrimination entropy [3]. r Biometric fusion. Systems which combine biometric features are well understood to offer increased performance [1]. It may be possible to use the measure of biometric feature information to quantify whether a given combination of features offers any advantage, or whether the fused features are largely redundant. The example of fusion of FLD and PCA (200 features) given here clearly falls into the latter category, since it does not necessarily offer double the amount of information. r Novel biometric features. Many novel biometric features have been suggested, but it is often unclear whether a given feature offers much in the way of identi able information. Biometric information measurement may offer a way to validate the potential of such features. r Privacy protection. It would be useful to quantify the threat to privacy posed by the release of biometric feature information, and it would also be helpful
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Measuring Information Content in Biometric Features
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to be able to quantify the value of technologies to preserve privacy, such as algorithms to de-identify face images [26, 27].
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1. A. Ross and A. Jain, Information fusion in biometrics, Pattern Recognit. Lett. 24:2115 2125, 2003. 2. J. S. Wayman, The cotton ball problem, Biometrics Conference, Washington DC, September 20 22, 2004. 3. J. Daugman, The importance of being random: Statistical principles of iris recognition, Pattern Recognit. 36:279 291, 2003. 4. T. M. Cover and J. A. Thomas, Elements of Information Theory, John Wiley & Sons, New York, 1991. 5. M. Golfarelli, D. Maio, and D. Maltoni, On the error-reject tradeoff in biometric veri cation systems, IEEE Trans. Pattern Anal. Mach. Intell. 19:786 796, 1997. 6. A. Adler, R. Youmaran, and S. Loyka, Information content of biometric features, in Biometrics Consortium Conference Washington, DC, September 19 21, 2005. 7. W. Zhao, R. Chellappa, P. J. Philips, and A. Rosenfeld, Face recognition: a literature survey, ACM Comput. Surveys 35:399 458, 2003. 8. ISO JTC1 SC37 Biometrics, ISO 29794-1 biometric sample quality, Committee Draft 1, August 10, 2007. 9. E. Tabassi, C. R. Wilson, and C. I. Watson, Fingerprint image quality, NISTIR 7151, August 2004. 10. A. Adler, R. Youmaran, and S. Loyka, Towards a measure of biometric feature information, Pattern Anal. Appli., 2008. 11. B. A. Draper, K. Baek, M. S. Bartlett, and J. R. Beveridge, Recognizing faces with PCA and ICA, Comput. Vis. Image Understanding 91:115 137, 2003. 12. P. Grother, Software tools for an eigenface implementation, National Institute of Standards and Technology, 2000, http://www.nist.gov/humanid/feret/ 13. O. Alter, P. O. Brown, and D. Botstein, Singular value decomposition for genomewide expression data processing and modeling, Proc. Natl. Acad. Sci. 97:10101 10106, 2000. 14. I. Craw, N. P. Costen, T. Kato, and S. Akamatsu, How should we represent faces for automatic recognition IEEE Trans. Pattern Anal. Mach. Intell. 21:725 736, 1999. 15. C. Xiang, X. A. Fan, and T. H. Lee, Face recognition using recursive Fisher linear discriminant, Conference on Communications, Circuits and Systems International, June 27 29, 2004. 16. M. Turk and A. Pentland, Eigenfaces for recognition, J. Cognit. Neurosci. 3:71 86, 1991. 17. S. Li and A. Jain, editors, Handbook of Face Recognition. Springer, Berlin, 2005. 18. T. W. Lee, Nonlinear approaches to independent component analysis, Proceedings of the American Institute of Physics, 1999. 19. P. J. Phillips, P. Grother, R. J. Micheals, D. M. Blackburn, E. Tabassi, and J. M. Bone, FRVT 2002: Evaluation Report, NIST, March 2003, http://www.frvt.org/DLs/FRVT_2002_Evaluation_Report.pdf 20. P. J. Phillips, T. W. Scruggs, A. J. O Toole, P. J. Flynn, K. W. Bowyer, C. L. Svhott, and M. Sharpe, FRVT 2006: Evaluation Report, NIST, March 2007, http://www.frvt.org/ FRVT2006/docs/FRVT2006andICE2006LargeScaleReport.pdf. 21. G. Doddington, W. Liggett, A. Martin, M. Przybocki, and D. Reynolds, Sheep, goats, lambs, and wolves: An analysis of individual differences in speaker recognition performance, in Proceedings of the International Conference on Auditory Visual Speech Processing, Sidney, Australia, November 1998. 22. S. Pankanti, S. Prabhakar, and A. K. Jain, On the individuality of ngerprints, IEEE Trans. Pattern Anal. Mach. Intell. 24:1010 1025, 2002. 23. U. Uludag, S. Pankanti, S. Prabhakar, and A. K. Jain, Biometric cryptosystems: Issues and challenges, Proc. IEEE 92:948 960, 2004. 24. L. Ballard, S. Kamara, F. Monrose, and M. Reiter, On the Requirements of Biometric Key Generators, Technical Report TR-JHU-SPAR-BKMR-090707, John Hopkins University, 2007.
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