Abstract
Biometrics are increasingly being used as security measures in online as well as offline systems, giving rise to more reliable and unique authentication techniques. In these systems, false positive minimization is one of the crucial requirements, which is especially critical in security sensitive applications. In this chapter, we present an Extended Metacognitive Neuro-Fuzzy Inference System (eMcFIS) based biometric identification system. eMcFIS consists of a cognitive component and a metacognitive component. The cognitive component, which is a neuro-fuzzy inference system, learns the input-output relationship efficiently. The metacognitive component is a self-regulatory learning mechanism, which actively regulates the learning in the cognitive component such that the network avoids over-fitting the training samples. Further, the learning strategies are chosen such that the network minimizes false-positive prediction. The proposed eMcFIS is first benchmarked on a set of medical datasets from machine learning databases. eMcFIS is then employed in detection of two real-world biometric security applications, signature verification and fingerprint recognition. The performance comparison with other state-of-the-art authentication systems clearly highlights the advantages of the proposed approach.
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References
Sahami, M., Dumais, S., Heckerman, D., Horvitz, E.: A bayesian approach to filtering junk e-mail. AAAI Technical Report (1998)
Androutsopoulos, I., Koutsias, J., Chandrinos, K.V., Spyropoulos, C.D.: An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 160–167. Athens (2000)
Schneider, K.-M.: A comparison of event models for Naive Bayes anti-spam e-mail filtering. In: Proceedings of the 10th Conference on European Chapter of the Association for Computational Linguistics, vol. 1, pp. 307–314. Budapest (2003)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)
Kołcz, A., Alspector, J.: SVM-based filtering of e-mail spam with content-specific misclassification costs. In: Proceedings of the Workshop on Text Mining, pp. 1–14. San Jose (2001)
Sculley, D., Wachman, G.M.: Relaxed online SVMs for spam filtering. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 415–422. Amsterdam (2007)
Carreras, X., Marquez, L., Salgado, J.G.: Boosting trees for anti-spam email filtering. In: Proceedings of 4th International Conference on Recent Advances in Natural Language Processing, pp. 58–64. Tzigov Chark (2001)
Wu, J., Mullin, M.D., Rehg, J.M.: Linear asymmetric classifier for cascade detectors. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 988–995. Bonn (2005)
Yih, W.-T., Goodman, J., Hulten, G.: Learning at low false positive rates. In: Proceedings of the 3rd Conference on Email and Anti-Spam, pp. 1–8. Mountain View (2006)
Lynam, T.R., Cormack, G.V., Cheriton, D.R.: On-line spam filter fusion. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 123–130. Seattle (2006)
Bratko, A., Cormack, G.V., Filipicˇ, B., Lynam, T.R., Zupan, B.: Spam filtering using statistical data compression models. J. Mach. Learn. Res. 7, 2673–2698 (2006)
Akusok, A., Miche, Y., Hegedus, J., Nian, R., Lendasse, A.: A two-stage methodology using k-NN and false-positive minimizing ELM for nominal data classification. Cognit. Comput. 6(3), 432–445 (2014)
Huang, G.-B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(2), 513–529 (2012)
Cherkassky, V.: Fuzzy inference systems: a critical review. In: Kaynak, O., Zadeh, L.A., Türkşen, B., Rudas, I.J. (eds.) Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications. NATO ASI Series, vol. 162, pp. 177–197. Springer, Heidelberg (1998)
Jang, J.-S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)
Kasabov, N.: Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning. IEEE Trans. Syst. Man Cybern. Part B Cybern. 31(6), 902–918 (2001)
Kasabov, N.K., Song, Q.: DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans. Fuzzy Syst. 10(2), 144–154 (2002)
Lin, C.-T., Yeh, C.-M., Liang, S.-F., Chung, J.-F., Kumar, N.: Support-vector-based fuzzy neural network for pattern classification. IEEE Trans. Fuzzy Syst. 14(1), 31–41 (2006)
Juang, C.-F., Chiu, S.-H., Chang, S.-W.: A self-organizing TS-type fuzzy network with support vector learning and its application to classification problems. IEEE Trans. Fuzzy Syst. 15(5), 998–1008 (2007)
Rong, H.-J., Huang, G.-B., Sundararajan, N., Saratchandran, P.: Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans. Syst. Man Cybern. Part B Cybern. 39(4), 1067–1072 (2009)
Subramanian, K., Suresh, S., Sundararajan, N.: A Metacognitive neuro-fuzzy inference system (McFIS) for sequential classification problems. IEEE Trans. Fuzzy Syst. 21(6), 1080–1095 (2013)
Isaacson, Y.M., Fujita, F.: Metacognitive knowledge monitoring and self-regulated learning: Academic success and reflections on learning. J. Scholarsh. Teach. Learn. 6(1), 39–55 (2006)
Subramanian, K., Savitha, R., Suresh, S.: A metacognitive complex-valued interval type-2 fuzzy inference system. IEEE Transa. Neural Netw. Learn. Syst. 25(9), 1659–1672 (2014)
Suresh, S., Dong, K., Kim, H.J.: A sequential learning algorithm for self-adaptive resource allocation network classifier. Neurocomputing 73(16), 3012–3019 (2010)
Sateesh Babu, G., Suresh, S.: Meta-cognitive Neural Network for classification problems in a sequential learning framework. Neurocomputing 81, 86–96 (2012)
Subramanian, K., Suresh, S.: Human action recognition using meta-cognitive neuro-fuzzy inference system. Int. J. Neural Syst. 22(6), 1–15 (2012)
Angelov, P.P., Zhou, X.: Evolving fuzzy-rule-based classifiers from data streams. IEEE Trans. Fuzzy Syst. 16(6), 1462–1475 (2008)
Rong, H.-J., Sundararajan, N., Huang, G.-B., Saratchandran, P.: Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction. Fuzzy Sets Syst. 157(9), 1260–1275 (2006)
Zhang, T.: Statistical behavior and consistency of classification methods based on convex risk minimization. Ann. Stat. 32(1), 56–85 (2004)
Suresh, S., Sundararajan, N., Saratchandran, P.: Risk-sensitive loss functions for sparse multi-category classification problems. Inf. Sci. 178(12), 2621–2638 (2008)
Hoffmann, H.: Kernel PCA for novelty detection. Pattern Recognit. 40(3), 863–874 (2007)
Blake, C., Merz, C.: UCI repository of machine learning databases, Department of Information and Computing Sciences, University of California, Irvine, CA, USA. http://archive.ics.uci.edu/ml/ (1998)
Suckling, J., Parker, J., Dance, D.R., Astley, S., Hutt, I., Boggis, C.R.M., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S.-L., Taylor, P., Betal, D., Savage, J.: The mammographic image analysis society digital mammogram database. In: Exerpta Medica International Congress Series, vol. 1069, pp. 375–378 (1994)
Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K.: FVC2004: third fingerprint verification competition. In: Proceedings of the First International Conference on Biometric Authentication, pp. 1–7. Hong Kong (2004)
Ortega-Garcia, J., Fierrez-Aguilar, J., Simon, D., Gonzalez, J., Faundez-Zanuy, M., Espinosa, V., Satue, A., Hernaez, I., Igarza, J.-J., Vivaracho, C., Escudero, D., Moro, Q.-I.: MCYT baseline corpus: a bimodal biometric database. IEE Proc. Vis. Image Signal Process. 150(6), 395–401 (2003)
Impedovo, D., Pirlo, G.: Automatic signature verification: the state of the art. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 38(5), 609–635 (2008)
Xiao, X., Leedham, G.: Signature verification using a modified Bayesian network. Pattern Recognit. 35(5), 983–995 (2002)
Xiao, X.-H., Leedham, G.: Signature Verification by neural networks with selective attention. Appl. Intell. 11(2), 213–223 (1999)
Al-Shoshan, A.I.: Handwritten signature verification using image invariants and dynamic features. In: International Conference on Computer Graphics, Imaging and Visualisation, pp. 173–176. Sydney (2006)
Franke, K., Zhang, Y.-N., Köppen, M.: Static signature verification employing a Kosko-Neuro-fuzzy approach. In: Pal, N.R., Sugeno, M. (eds.) Advances in Soft Computing. Lecture Notes in Computer Science, vol. 2275, pp. 185–190. Springer, Heidelberg (2002)
Quek, C., Zhou, R.W.: Antiforgery: a novel pseudo-outer product based fuzzy neural network driven signature verification system. Pattern Recognit. Lett. 23(14), 1795–1816 (2002)
Van, B.L., Garcia-Salicetti, S., Dorizzi, B.: On using the Viterbi path along with HMM likelihood information for online signature verification. IEEE Trans. Syst. Man Cybern. B Cybern. 37(5), 1237–1247 (2007)
Yang, L., Widjaja, B.K., Prasad, R.: Application of hidden Markov models for signature verification. Pattern Recognit. 28(2), 161–170 (1995)
Kholmatov, A., Yanikoglu, B.: Identity authentication using improved online signature verification method. Pattern Recognit. Lett. 26(15), 2400–2408 (2005)
Ferrer, M.A., Alonso, J.B., Travieso, C.M.: Offline geometric parameters for automatic signature verification using fixed-point arithmetic. IEEE Trans. Pattern Anal. Mach. Intell. 27(6), 993–997 (2005)
Han, K., Sethi, I.K.: Handwritten signature retrieval and identification. Pattern Recognit. Lett. 17(1), 83–90 (1996)
Huang, K., Yan, H.: Stability and style-variation modeling for on-line signature verification. Pattern Recognit. 36(10), 2253–2270 (2003)
Bovino, L., Impedovo, S., Pirlo, G., Sarcinella, L.: Multi-expert verification of hand-written signatures. In: Proceedings of Seventh International Conference on Document Analysis and Recognition, pp. 932–936. Edinburgh (2003)
Di Lecce, V., Dimauro, G., Guerriero, A., Impedovo, S., Pirlo, G., Salzo, A.: A multi-expert system for dynamic signature verification. In: Kittler, J., Roli, F. (eds.) Multiple Classifier Systems. Lecture Notes in Computer Science, vol. 1857, pp. 320–329. Springer, Heidelberg (2000)
Fierrez-Aguilar, J., Nanni, L., Lopez-Peñalba, J., Ortega-Garcia, J., Maltoni, D.: An on-line signature verification system based on fusion of local and global information. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) Audio- and Video-Based Biometric Person Authentication. Lecture Notes in Computer Science, vol. 3546, pp. 523–532. Springer, Heidelberg (2005)
Nanni, L., Lumini, A.: An experimental comparison of ensemble of classifiers for biometric data. Neurocomputing 69(13–15), 1670–1673 (2006)
Maltoni, D.: A tutorial on fingerprint recognition. In: Tistarelli, M., Bigun, J., Grosso, E. (eds.) Advanced Studies in Biometrics. Lecture Notes in Computer Science, vol. 3161, pp. 43–68. Springer, Heidelberg (2005)
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Fingerprint classification and indexing. Handbook of Fingerprint Recognition, pp. 235–269. Springer, London (2009)
Wang, L., Dai, M.: Application of a new type of singular points in fingerprint classification. Pattern Recognit. Lett. 28(13), 1640–1650 (2007)
Neuhaus, M., Bunke, H.: A graph matching based approach to fingerprint classification using directional variance. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) Audio- and Video-Based Biometric Person Authentication. Lecture Notes in Computer Science, vol. 3546, pp. 191–200. Springer, Heidelberg (2005)
Hong, J.-H., Min, J.-K., Cho, U.-K., Cho, S.-B.: Fingerprint classification using one-vs-all support vector machines dynamically ordered with naive Bayes classifiers. Pattern Recognit. 41(2), 662–671 (2008)
Majumdar, A., Ward, R.K.: Fingerprint recognition with curvelet features and fuzzy KNN classifier. In: Proceedings of IASTED Conference on Signal and Image Processing, pp. 243–248. Kailua-Kona (2008)
Altun, A.A., Allahverdi, N.: Recognition of fingerprints enhanced by contourlet transform with artificial neural networks. In: 28th International Conference on Information Technology Interfaces, pp. 167–172. Cavtat (2006)
Gupta, J.K., Kumar, R.: An efficient ANN based approach for latent fingerprint matching. Int. J. Comput. Appl. 7(10), 18–21 (2010)
Yao, Y., Marcialis, G.L., Pontil, M., Frasconi, P., Roli, F.: Combining flat and structured representations for fingerprint classification with recursive neural networks and support vector machines. Pattern Recognit. 36(2), 397–406 (2003)
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Bologna, G., Appel, R.D.: A comparison study on protein fold recognition. In: Proceedings of the 9th International Conference on Neural Information Processing, vol. 5, pp. 2492–2496. Singapore (2002)
Martinez-Munoz, G., Suárez, A.: Switching class labels to generate classification ensembles. Pattern Recognit. 38(10), 1483–1494 (2005)
Melville, P., Mooney, R.J.: Creating diversity in ensembles using artificial data. J. Inf. Fusion Spec. Issue Divers. Multi Classif. Syst. 6(1), 99–111 (2005)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2000)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)
Acknowledgement
The authors would like to thank L. Nanni for sharing the biometric fingerprint and signature verification datasets and the code for equal error rate computation used in performance comparison.
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Padmanabhuni, B.M., Subramanian, K., Sundaram, S. (2016). Extended Metacognitive Neuro-Fuzzy Inference System for Biometric Identification. In: Abielmona, R., Falcon, R., Zincir-Heywood, N., Abbass, H. (eds) Recent Advances in Computational Intelligence in Defense and Security. Studies in Computational Intelligence, vol 621. Springer, Cham. https://doi.org/10.1007/978-3-319-26450-9_12
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