zbMATH — the first resource for mathematics

Examples
Geometry Search for the term Geometry in any field. Queries are case-independent.
Funct* Wildcard queries are specified by * (e.g. functions, functorial, etc.). Otherwise the search is exact.
"Topological group" Phrases (multi-words) should be set in "straight quotation marks".
au: Bourbaki & ti: Algebra Search for author and title. The and-operator & is default and can be omitted.
Chebyshev | Tschebyscheff The or-operator | allows to search for Chebyshev or Tschebyscheff.
"Quasi* map*" py: 1989 The resulting documents have publication year 1989.
so: Eur* J* Mat* Soc* cc: 14 Search for publications in a particular source with a Mathematics Subject Classification code (cc) in 14.
"Partial diff* eq*" ! elliptic The not-operator ! eliminates all results containing the word elliptic.
dt: b & au: Hilbert The document type is set to books; alternatively: j for journal articles, a for book articles.
py: 2000-2015 cc: (94A | 11T) Number ranges are accepted. Terms can be grouped within (parentheses).
la: chinese Find documents in a given language. ISO 639-1 language codes can also be used.

Operators
a & b logic and
a | b logic or
!ab logic not
abc* right wildcard
"ab c" phrase
(ab c) parentheses
Fields
any anywhere an internal document identifier
au author, editor ai internal author identifier
ti title la language
so source ab review, abstract
py publication year rv reviewer
cc MSC code ut uncontrolled term
dt document type (j: journal article; b: book; a: book article)
Fusing multimodal biometrics with quality estimates via a Bayesian belief network. (English) Zbl 1132.68649
Summary: Biometric systems for today’s high security applications must meet stringent performance requirements; fusing multiple biometrics can help lower system error rates. Fusion methods include processing biometric modalities sequentially until an acceptable match is obtained, using logical (AND/OR) operations, or summing similarity scores. More sophisticated methods combine scores from separate classifiers for each modality. This paper develops a novel fusion architecture based on Bayesian belief networks. Although Bayesian update methods have been used before, our approach more fully exploits the graphical structure of Bayes nets to define and explicitly model statistical dependencies between relevant variables: per sample measurements, such as match scores and corresponding quality estimates, and global decision variables. These statistical dependencies are in the form of conditional distributions which we model as Gaussian, gamma, log-normal or beta, each of which is determined by its mean and variance, thus significantly reducing training data requirements. Moreover, by conditioning decision variables on quality as well as match score, we can extract information from lower quality measurements rather than rejecting them out of hand. Another feature of our method is a global quality measure designed to be used as a confidence estimate supporting decision making. Preliminary studies using the architecture to fuse fingerprints and voice are reported.

MSC:
68T10Pattern recognition, speech recognition
WorldCat.org
Full Text: DOI
References:
[1] Jain, A. K.; Prabhakar, S.; Chen, S.: Combining multiple matchers for a high security fingerprint verification system. Pattern recognition lett. 20, 1371-1379 (1999)
[2] Ross, A.; Jain, A. K.: Information fusion in biometrics. Pattern recognition lett. 24, 2115-2125 (2003)
[3] Kittler, J.: On combining classifiers. IEEE trans. Pattern anal. Mach intell. 20, No. 3, 226-239 (1998)
[4] Bigun, J.: Multimodal biometric authentication using quality signals in mobile communications. Proceedings of IAPR international conference on image analysis and processing (ICIAP), 2-13 (2003)
[5] Rukhin, A. L.; Malioutov, I.: Fusion of biometric algorithms in the recognition problem. Pattern recognition lett. 26, 679-684 (2005)
[6] Verlinde, P.; Chollet, G.; Acheroy, M.: Multimodal identity verification using expert fusion. Inf. fusion 1, No. 1, 17-33 (2000)
[7] J. Fierrez-Aguilar, J. Ortega-Garcia, J. Gonzalez-Rodriguez, Fusion strategies in multimodal biometric verification, in: International Conference, Proceedings on Multimedia and Expo (ICME ’03), vol. 3(6 -- 9), 2003, pp. 5 -- 8. · Zbl 1050.68722
[8] Fierrez-Aguilar, J.: Kernel-based multimodal biometric verification using quality signals. Biometric technology for human identification, Proceedings of the SPIE 5404, 544-554 (2004)
[9] B. Gutschoven, P. Verlinde, Multimodal identity verification using support vector machines (SVM), in: Proceedings of the Third International Conference on Information Fusion, vol. 2, 2000, pp. 3 -- 8.
[10] E. Tabassi, C. Wilson, C. Watson, Fingerprint image quality, Technical Report 7151, 2004, (Appendices for NISTIR 7151 can be found at \langle http://fingerprint.nist.gov/NFIS\rangle ).
[11] Y. Chen, S. Dass, A.J. Jain, Fingerprint quality indices for predicting authentication performance, in: T. Kanade, A.K. Jain, N.K. Ratha (Eds.), Fifth International Conference AVBPA Proceedings, Springer Lecture Notes in Computer Science, vol. 3546, 2005, pp. 160 -- 170.
[12] Wein, L. M.; Baveja, M.: Using fingerprint image quality to improve the identification performance of the U.S. Visitor and immigrant status indicator technology program. Proc. natl. Acad. sci. 102, No. 21, 7772-7775 (2005)
[13] K. Nandakumar, Y. Chen, A.K. Jain, S.C. Dass, Quality-based score level fusion in multibiometric systems, in: Proceedings of the 18th International Conference on Pattern Recognition (ICPR06), 2006, pp. 473 -- 476.
[14] Fierrez-Aguilar, J.; Ortega-Garcia, J.; Gonzales-Rodriguez, J.: Discriminative multimodal biometric authentication based on quality measures. Pattern recognition 38, 777-779 (2005)
[15] Baker, J. P.; Maurer, D. E.: Fusion of biometric data with quality estimates via a Bayesian belief network. Proceedings of the biometric symposium, 21-22 (2005)
[16] J. Richiardi, P. Prodanov, A. Drygajlo, A probabilistic measure of modality reliability in speaker verification, in: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP ’05, vol. 1, 2005, pp. 709 -- 712.
[17] Teoh, A. B. J.; Samad, S. A.; Hussain, A.: A face and speech biometric verification system using a simple Bayesian structure. J. inf. Sci. eng. 21, 1121-1137 (2005)
[18] E.S. Bigun, J. Bigun, B. Duc, S. Fischer, Expert conciliation for multi modal person authentication systems by Bayesian statistics, in: J. Bigun, G. Chollet, G. Borgefors (Eds.), First International Conference AVBPA Proceedings, Springer Lecture Notes in Computer Science, vol. 1206, 1997, pp. 291 -- 300.
[19] Jensen, F. V.: Bayesian networks and decision graphs. (2001) · Zbl 0973.62005
[20] Prabhakar, S.; Jain, A. K.: Decision-level fusion in fingerprint verification. Pattern recognition 35, 861-874 (2002) · Zbl 0999.68579
[21] P. Domingos, M. Pazzani, Beyond independence conditions for the optimality of the simple Bayesian classifier, in: Proceedings of the 13th International Conference on Machine Learning (ICML 96), 1996, pp. 105 -- 112.
[22] S.C. Dass, K. Nandakumar, A.K. Jain, A principled approach to score level fusion in multimodal biometric systems, in: T. Kanade, A.K. Jain, N.K. Ratha (Eds.), Fifth International Conference AVBPA Proceedings, Springer Lecture Notes in Computer Science, vol. 3546, 2005, pp. 1049 -- 1058
[23] \langle http://www.int-evry.fr/biometrics/BMEC2007/\rangle .
[24] C. Watson, NIST Special Database 14: 8-bit Gray Scale Images of Mated Fingerprint Card Pairs 2, CD-ROM & Documentation, 1993.
[25] C. Watson, NIST Fingerprint Image Software 2 (NFIS2), Rel. 28-2.2, CDROM and Documentation, 2004.
[26] K. Messer, J. Matas, J. Kittler, J. Luettin, and G. Maitre, XM2VTSdb: The Extended M2VTS Database, in: Second International Conference AVBPA Proceedings, Washington, DC, 1999, pp. 166 -- 171 (Can be found at \langle http://www.ee.surrey.ac.uk/CVSSP/Publications\rangle ).
[27] N. Poh, S. Bengio, Database, protocol and tools for evaluating score-level fusion algorithms in biometric authentication, in: T. Kanade, A.K. Jain, N.K. Ratha (Eds.), Fifth International Conference AVBPA Proceedings, Springer Lecture Notes in Computer Science, vol. 3546, 2005, pp. 1059 -- 1070.
[28] National Institute of Standards and Technology (NIST) Speech Quality Assurance (SPQA) Package 2.3 Documentation, URL: \langle http://www.nist.gov/speech/tools/spqa_23sphere25tarZ.htm\rangle .
[29] T. Scheffer, R. Herbrich, Unbiased assessment of learning algorithms, in: Proceedings of the 15th International Joint Conference on Artificial Intelligence, 1997, pp. 798 -- 903.
[30] C.L. Wilson, M.D. Garris, C.I. Watson, Matching performance for the US_VISIT IDENT system using flat fingerprints, National Institute of Standards and Technology, NIST Internal Report 7110, 2004.
[31] C.L. Wilson, C.I. Watson, M.D. Garris, A. Hicklin, Using the NIST Verification Test Bed (VTB), National Institute of Standards and Technology, NIST Internal Report 7020, 2003.