Abstract
Multibiometric systems are a promising area that addresses a number of unimodal biometric systems drawbacks. The main limit of these systems is the lack of information in terms of quantity (number of discriminant features) and quality (diversity of information, correlation…). Using multiple sources of information and/or treatment is a solution to overcome these problems and enhance system performances. Performance requirements of current systems related to context use involve designed solutions that optimally satisfy security requirements. This can represent an optimization problem that aims at searching the optimal solution matching security needs. In our study, we are interested in combining different score level rules using an evolutionary algorithm. We use Genetic Algorithm to derive a score fusion function based on primitive operations. The process uses an optimized tree to determine function structure. We perform experiments on the XM2VTS score database based on a well-founded protocol for reliable results. The obtained results are promising and outperforms other fusion rules.
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References
Alajlan, N., Saiful Islam, M., Ammour, N.: Fusion of fingerprint and heartbeat biometrics using fuzzy adaptive genetic algorithm. In: World Congress on Internet Security, pp. 76–81 (2013)
Alsaade, F., Ariyaeeinia, A., Malegaonkar, A., Pillay, S.: Qualitative fusion of normalised scores in multimodal biometrics. Pattern Recognit. Lett. 30(5), 564–569 (2009)
Anzar, S.T.M., Sathidevi, P.S.: On combining multi-normalization and ancillary measures for the optimal score level fusion of fingerprint and voice biometrics. EURASIP J. Adv. Signal Process. 10, 1–17 (2014)
Barbosa, I.B., Theoharis, T., Schellewald, C., Athwal, C.: Transient biometrics using finger nails. In: Proceedings of 6th Biometrics: Theory, Applications and Systems (BTAS), 2013, pp. 1–6. Arlington, VA, USA, 29 Sept–2 Oct 2013
Bendris, M., Charlet, D., Chollet, G.: Introduction of quality measures in audio-visual identity verification. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1913–1916, Taipei, 19–24 Apr 2009
Bengio, S., Mariethoz, J.: The expected performance curve: a new assessment measure for person authentication. In: The Speaker and Language Recognition Workshop (Odyssey), pp. 279–284 (2004)
Eskandaria, M., Toygar, Ö.: Selection of optimized features and weights on face-iris fusion using distance images. Comput. Vis. Image Underst. 137, 63–75 (2015)
Giot, R., Rosenberger, C.: Genetic programming for multibiometrics. Expert Syst. Appl. 39, 1837–1847 (2012)
Kryszczuk, K., Richiardi, J., Prodanov, P., Drygajlo, A.: Reliability-based decision fusion in multimodal biometric verification systems. EURASIP J. Appl. Signal Process. 2007(1), 74 (2007)
Kumar, A., Kanhangad, V., Zhang, D.: A new framework for adaptive multimodal biometrics management. Inf. Forensics Secur. 5(1), 92–102 (2010)
Kumar, A., Ravikanth, C.: Personal authentication using finger knuckle surface. IEEE Trans. Inf. Forensics Secur. 4, 98–110 (2009)
Morizet, N., Gilles, J.: A new adaptive combination approach to score level fusion for face and iris biometrics combining wavelets and statistical moments. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part II. LNCS, vol. 5359, pp. 661–671. Springer, Heidelberg (2008)
Pal, S., Mukherjee, K., Majumder, B.P., Saha, C., Panigrahi, B.K., Das, S.: Differential evolution based score level fusion for multi-modal biometric systems. In: Computational Intelligence in Biometrics and Identity Management (CIBIM), pp. 38–44. Orlando, FL, USA, 9–12 Dec 2014)
Parviz, M., Moin, M.S.: Boosting Approach for score level fusion in multimodal biometrics based on AUC maximization. J. Inf. Hiding Multimedia Signal Process. 2(1), 51–59 (2011)
Poh, N., Bengio, S.: Database, protocol and tools for evaluating score-level fusion algorithms in biometric authentication. Pattern Recogn. 39(2), 223–233 (2006)
Poh, N., Bengio, S.: Improving fusion with margin-derived confidence in biometric authentication tasks. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 474–483. Springer, Heidelberg (2005)
Ross, A., Nandakumar, K., Jain, A.: Handbook of Multibiometrics. Springer, Heidelberg (2006)
Souvannavong, F., Merialdo, B., Huet, B.: Multi-modal classifier fusion for video shot content retrieval. In: Proceedings of WIAMIS (Eurecom, 8 avril 2005)
Srinivas, N., Veeramachaneni, K., Osadciw, L.A.: Fusing correlated data from multiple classifiers for improved biometric verification. In: Proceedings of 9th Information Fusion, pp. 1504–1511. Seattle, WA, USA, 6–9 July 2009
Kale, K.V., Rode, Y.S., Kazi, M.M., Dabhade, S.B., Chavan, S.V.: Multimodal biometric system using fingernail and finger knuckle. In: Computational and Business Intelligence (ISCBI), pp. 279–283. New Delhi, India, 24–26 Aug 2013
Unar, J.A., Seng, W.C., Abbasi, A.: A review of biometric technology along with trends and prospects. Pattern Recogn. 47, 279–283 (2014)
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Artabaz, S., Sliman, L., Benatchba, K., Dellys, H.N., Koudil, M. (2015). Score Level Fusion Scheme in Hybrid Multibiometric System. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2015. Lecture Notes in Computer Science(), vol 9429. Springer, Cham. https://doi.org/10.1007/978-3-319-25939-0_15
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DOI: https://doi.org/10.1007/978-3-319-25939-0_15
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