In a biometric authentication system using protected templates, a pseudonymous identifier is the part of a protected template that can be directly compared. Each compared pair of pseudonymous identifiers results in a decision testing whether both identifiers are derived from the same biometric characteristic. Compared to an unprotected system, most existing biometric template protection methods cause to a certain extent degradation in biometric performance. Fusion is therefore a promising way to enhance the biometric performance in template-protected biometric systems. Compared to feature level fusion and score level fusion, decision level fusion has not only the least fusion complexity, but also the maximum interoperability across different biometric features, template protection and recognition algorithms, templates formats, and comparison score rules. However, performance improvement via decision level fusion is not obvious. It is influenced by both the dependency and the performance gap among the conducted tests for fusion. We investigate in this paper several fusion scenarios (multi-sample, multi-instance, multi-sensor, multi-algorithm, and their combinations) on the binary decision level, and evaluate their biometric performance and fusion efficiency on a multi-sensor fingerprint database with 71,994 samples.
References
[1]
Juels, A.; Wattenberg, M. A fuzzy Commitment Scheme. Proceedings of the 6th ACM Conference on Computer and Communications Security, Singapore, 1–4 November 1999.
[2]
Juels, A.; Sudan, M. A Fuzzy Vault Scheme. Proceedings of IEEE International Symposium on Information Theory, Lausanne, Switzerland, 30 June–5 July 2002.
[3]
Teoh, A.B.J.; Goh, A.; Ngo, D.C.L. Random multispace quantisation as an analytic mechanism for biohashing of biometric and random identity inputs. IEEE Trans. Pattern Anal. Mach. Intell. 2006, 28, 1892–1901.
Boult, T.E.; Scheirer, W.J.; Woodworth, R. Revocable Fingerprint Biotokens: Accuracy and Security Analysis. Proceedings of IEEE International Conference on Computer Visionand Pattern Recognition, Minneapolis, MN, USA, 18–23 June 2007.
Bringer, J.; Despiegel, V. Binary Feature Vector Fingerprint Representation from Minutiae Vicinities. Proceedings of IEEE 4th International Conference on Biometrics: Theory, Applications, and Systems, Washington, DC, USA, 27–29 September 2010.
[8]
Yang, B.; Busch, C. Dynamic Random Projection for Biometric Template Protection. Proceedings of IEEE 4th International Conference on Biometrics: Theory, Applications, and Systems, Washington, DC, USA, 27–29 September 2010.
[9]
Dodis, Y.; Ostrovsky, R.; Reyzin, L.; Smith, A. Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data. SIAM J. Comput. 2008, 38, 97–139. Preliminary version.Adv. Cryptol.—EUROCRYPT 2004 2004, 3027, 523–540.
[10]
Sutcu, Y.; Li, Q.; Memon, N. Protecting biometric templates with sketch: Theory and practice. IEEE Trans. Inf. Forensics Secur. 2007, 2, 503–512.
[11]
Information Technology—Security Techniques—Biometric Information Protection. ISO/IEC IS 24745; International Organization for Standardization: Geneva, Switzerland, 2011.
[12]
Jain, A.K.; Nandakumar, K.; Nagar, A. Biometric Template Security. EURASIP J. Adv. Signal Process. 2008, 2008, doi:10.1155/2008/579416.
[13]
Nagara, A.; Nandakumar, K.; Jain, A.K. Biometric Template Transformation: A Security Analysis. Proceedings of the SPIE Media Forensics and Security, Electronic Imaging, San Jose, USA, 17–21 January 2010.
[14]
Information Technology—Biometrics—Multimodal and Other Multibiometric Fusion. ISO/IEC TR 24722; International Organization for Standardization: Geneva, Switzerland, 2007.
[15]
EU-FP7 project. Trusted Revocable Biometric Identities. Available online: http://www.turbine-project.eu/ (accessed on 21 February 2012).
[16]
Simoens, K. TURBINE Security Assessment: How to Build Trust. Available online: http://www.turbine-project.eu/dowloads/Day1.4.3.TURBINE_Workshop_17012011_K.Simoens.pdf (accessed on 21 February 2012).
[17]
GUC100 Multi-sensor Fingerprint Database. Available online: http://www.nislab.no/guc100/ (accessed on 21 February 2012).
[18]
Information Technology—Biometric Data Interchange Formats—Part 2: Finger Minutiae Data. ISO/IEC IS 19794-2; International Organization for Standardization: Geneva, Switzerland, 2007.
[19]
Simoens, K.; Tuyls, P.; Preneel, B. Privacy Weaknesses in Biometric Sketches. Proceedings of the 30th IEEE Symposium on Security and Privacy, Oakland, CA, USA, 17–20 May 2009; pp. 188–203.
[20]
Kelkboom, E.J.C.; Breebaart, J.; Kevenaar, T.A.M.; Buhan, I.; Veldhuis, R.N.J. Preventing the Decodability Attack Based Cross-Matching in a Fuzzy Commitment Scheme. IEEE Trans. Inf. Forensics Secur. 2011, 6, 107–121.
[21]
Scheirer, W.J.; Boult, T.E. Cracking Fuzzy Vaults and Biometric Encryption. Proceedings of the 2007 Biometrics Symposium, Baltimore, MD, USA, 11–13 September 2007.
[22]
Mihailescu, P.; Munk, A.; Tams, B. The Fuzzy Vault for Fingerprints is Vulnerable to Brute Force Attack. Proceedings of the BIOSIG 2009, Darmstadt, Germany, 17–18 September 2009; pp. 43–54.
[23]
Bringer, J.; Chabanne, H.; Kevenaar, T.A.M.; Kindarji, B. Extending Match-On-Card to Local Biometric Identification. Proceedings of the Biometric ID Management and Multimodal Communication, BioID-Multicomm 2009. LNCS 5707; 2009; pp. 178–186.
[24]
He, M.; Horng, S.J.; Fan, P.; Run, R.S.; Chen, R.J.; Lai, J.L.; Khan, M.K.; Sentosa, K.O. Performance evaluation of score level fusion in multimodal biometric systems. Pattern Recognit. 2010, 43, 1789–1800.
[25]
Daugman, J. Combining Multiple Biometrics. Available online: http://www.cl.cam.ac.uk/~jgd1000/combine/combine.html/ (accessed on 21 February 2012).
[26]
Prabhakar, S.; Jain, A.K. Decision-level fusion in biometric verification. Pattern Recognit. 2002, 35, 861–874.
[27]
NIST Fingerprint Image Quality (NFIQ). Available online: http://www.nist.gov/itl/iad/ig/nbis.cfm/ (accessed on 9 April 2012).
