Abstract
To uphold the security of individuals, analyzing the amount of information in binary biometric representation is highly essential. While Shannon entropy is a measure to quantify the expected value of information in the binary representation, it does not account the extent to which every binary representation could distinctively identify a person in a population. Hence, it does not appropriately quantify the hardness of obtaining a close approximation of the user’s biometric template if one maliciously leverages the population distribution. To resolve this, relative entropy has been used to measure information of user distribution with reference to the population distribution. However, existing relative-entropy estimation techniques that are based on statistical methods in the Euclidean space cannot be directly extended to the Hamming space. Therefore, we put forward a new entropy measure known as distance entropy and its estimation technique to quantify the information in binary biometric representation more effectively with respect to the discrimination power of the binary representation.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Alder, A.: Towards a Measure of Biometric Information. In: Canadian Conference on Electrical and Computer Engineering, pp. 210–213 (2006)
Adler, A., Youmaran, R., Loyka, S.: Information content of biometric features. In: Biometrics Consortium Conference, pp. 19–21 (2005)
Adler, A., Youmaran, R., Loyka, S.: Towards a Measure of Biometric Feature Information. Pattern Analysis and Applications 12(3), 261–270 (2009)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. on PAMI 19(7), 711–720 (1997)
Chen, C., Veldhuis, R.: Extracting Biometric Binary Strings with Minimal Area Under the FRR Curve for the Hamming Distance Classifier. In: 17th European Signal Processing Conference (EUSIPCO 2009), pp. 50–54 (2009)
Chen, C., Veldhuis, R.: Binary Biometric Representation through Pairwise Adaptive Phase Quantization. EURASIP Journal on Information Security (2011)
Cover, T., Thomas, J.: Entropy, relative entropy and mutual information. In: Elements of Information Theory, pp. 12–49. Wiley, New York (1991)
Daugman, J.: The importance of being random: Statistical principles of iris recognition. Pattern Recognition 36(2), 279–291 (2003)
Feng, Y.C., Yuen, P.C.: Binary Discriminant Analysis for Generating Binary Face Template. IEEE Transactions on Information Forensics and Security 7(2), 613–624 (2012)
Feng, Y.C., Yuen, P.C., Jain, A.K.: A Hybrid Approach for Generating Secure and Discriminating Face Template. IEEE Transactions on Information Forensics and Security 5(1), 103–117 (2010)
Kullback, S., Leibler, R.: On information and sufficiency. Annals of Mathematical Statistics 22(1), 79–86 (1951)
Lim, M.-H., Teoh, A.B.J.: An Effective Biometric Discretization Approach to Extract Highly Discriminative, Informative and Privacy-Protective Binary Representation. EURASIP Journal on Advances in Signal Processing (107) (2011)
Lim, M.-H., Teoh, A.B.J.: Discriminative and Non-user Specific Binary Biometric Representation via Linearly-Separable Subcode Encoding-based Discretization. KSII Trans. on Internet and Information Systems 5(2), 374–3890 (2011)
Lim, M.-H., Teoh, A.B.J., Toh, K.A.: An Analysis on Linearly Separable SubCode-based Equal Width Discretization and its Performance Resemblances. EURASIP Journal on Advances in Signal Processing (82) (2011)
Lim, M.-H., Teoh, A.B.J., Toh, K.A.: An efficient dynamic reliability-dependent bit allocation for biometric discretization. Pattern Recognition 45(5), 1960–1971 (2012)
Nagar, A., Nandakumar, K., Jain, A.K.: A Hybrid Biometric Cryptosystem for Securing Fingerprint Minutiae Templates. Pattern Recognition Letters (2009)
Nanni, L., Brahnam, S., Lumini, A.: Biohashing applied to orientation-based minutia descriptor for secure fingerprint authentication system. Electronics Letters 47(15), 851–853 (2011)
Shannon, C.: A mathematical theory of communication. Bell System Technical Journal 27(3), 379–423 (1948)
Sutcu, Y., Sencar, H., Memon, N.: How to Measure Biometric Information. In: International Conference on Pattern Recognition (2010)
Teoh, A.B.J., Goh, A., Ngo, D.C.L.: Random Multispace Quantization as an Analytic Mechanism for BioHashing of Biometric and Random Identity Inputs. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 1892–1901 (2006)
Teoh, A.B.J., Yip, W.K., Lee, S.: Cancelable biometrics and annotations on BioHash. Pattern Recognition 41(6), 2034–2044 (2008)
Yue, F., Zuo, W., Zhang, D., Wang, K.: Orientation selection using modified FCM for competitive code-based palmprint recognition. Pattern Recognition 4(11), 2841–2849 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Feng, Y.C., Yuen, P.C., Lim, MH. (2012). Distance Entropy as an Information Measure for Binary Biometric Representation. In: Zheng, WS., Sun, Z., Wang, Y., Chen, X., Yuen, P.C., Lai, J. (eds) Biometric Recognition. CCBR 2012. Lecture Notes in Computer Science, vol 7701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35136-5_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-35136-5_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35135-8
Online ISBN: 978-3-642-35136-5
eBook Packages: Computer ScienceComputer Science (R0)