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Extended Metacognitive Neuro-Fuzzy Inference System for Biometric Identification

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Recent Advances in Computational Intelligence in Defense and Security

Part of the book series: Studies in Computational Intelligence ((SCI,volume 621))

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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

  1. Sahami, M., Dumais, S., Heckerman, D., Horvitz, E.: A bayesian approach to filtering junk e-mail. AAAI Technical Report (1998)

    Google Scholar 

  2. 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)

    Google Scholar 

  3. 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)

    Google Scholar 

  4. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)

    Article  Google Scholar 

  5. 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)

    Google Scholar 

  6. 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)

    Google Scholar 

  7. 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)

    Google Scholar 

  8. 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)

    Google Scholar 

  9. 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)

    Google Scholar 

  10. 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)

    Google Scholar 

  11. 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)

    MathSciNet  MATH  Google Scholar 

  12. 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)

    Article  Google Scholar 

  13. 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)

    Article  Google Scholar 

  14. 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)

    Google Scholar 

  15. Jang, J.-S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)

    Article  Google Scholar 

  16. 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)

    Article  Google Scholar 

  17. 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)

    Article  Google Scholar 

  18. 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)

    Article  Google Scholar 

  19. 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)

    Article  Google Scholar 

  20. 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)

    Article  Google Scholar 

  21. 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)

    Article  Google Scholar 

  22. 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)

    Google Scholar 

  23. 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)

    Article  Google Scholar 

  24. Suresh, S., Dong, K., Kim, H.J.: A sequential learning algorithm for self-adaptive resource allocation network classifier. Neurocomputing 73(16), 3012–3019 (2010)

    Article  Google Scholar 

  25. Sateesh Babu, G., Suresh, S.: Meta-cognitive Neural Network for classification problems in a sequential learning framework. Neurocomputing 81, 86–96 (2012)

    Article  Google Scholar 

  26. Subramanian, K., Suresh, S.: Human action recognition using meta-cognitive neuro-fuzzy inference system. Int. J. Neural Syst. 22(6), 1–15 (2012)

    Article  Google Scholar 

  27. Angelov, P.P., Zhou, X.: Evolving fuzzy-rule-based classifiers from data streams. IEEE Trans. Fuzzy Syst. 16(6), 1462–1475 (2008)

    Article  Google Scholar 

  28. 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)

    Article  MathSciNet  MATH  Google Scholar 

  29. Zhang, T.: Statistical behavior and consistency of classification methods based on convex risk minimization. Ann. Stat. 32(1), 56–85 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  30. Suresh, S., Sundararajan, N., Saratchandran, P.: Risk-sensitive loss functions for sparse multi-category classification problems. Inf. Sci. 178(12), 2621–2638 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  31. Hoffmann, H.: Kernel PCA for novelty detection. Pattern Recognit. 40(3), 863–874 (2007)

    Article  MATH  Google Scholar 

  32. 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)

  33. 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)

    Google Scholar 

  34. Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  35. 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)

    Google Scholar 

  36. 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)

    Article  Google Scholar 

  37. 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)

    Article  Google Scholar 

  38. Xiao, X., Leedham, G.: Signature verification using a modified Bayesian network. Pattern Recognit. 35(5), 983–995 (2002)

    Article  MATH  Google Scholar 

  39. Xiao, X.-H., Leedham, G.: Signature Verification by neural networks with selective attention. Appl. Intell. 11(2), 213–223 (1999)

    Article  Google Scholar 

  40. 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)

    Google Scholar 

  41. 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)

    Google Scholar 

  42. 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)

    Article  MATH  Google Scholar 

  43. 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)

    Article  Google Scholar 

  44. Yang, L., Widjaja, B.K., Prasad, R.: Application of hidden Markov models for signature verification. Pattern Recognit. 28(2), 161–170 (1995)

    Article  Google Scholar 

  45. Kholmatov, A., Yanikoglu, B.: Identity authentication using improved online signature verification method. Pattern Recognit. Lett. 26(15), 2400–2408 (2005)

    Article  Google Scholar 

  46. 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)

    Article  Google Scholar 

  47. Han, K., Sethi, I.K.: Handwritten signature retrieval and identification. Pattern Recognit. Lett. 17(1), 83–90 (1996)

    Article  Google Scholar 

  48. Huang, K., Yan, H.: Stability and style-variation modeling for on-line signature verification. Pattern Recognit. 36(10), 2253–2270 (2003)

    Article  MATH  Google Scholar 

  49. 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)

    Google Scholar 

  50. 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)

    Google Scholar 

  51. 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)

    Google Scholar 

  52. Nanni, L., Lumini, A.: An experimental comparison of ensemble of classifiers for biometric data. Neurocomputing 69(13–15), 1670–1673 (2006)

    Article  MATH  Google Scholar 

  53. 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)

    Google Scholar 

  54. Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Fingerprint classification and indexing. Handbook of Fingerprint Recognition, pp. 235–269. Springer, London (2009)

    Google Scholar 

  55. Wang, L., Dai, M.: Application of a new type of singular points in fingerprint classification. Pattern Recognit. Lett. 28(13), 1640–1650 (2007)

    Article  Google Scholar 

  56. 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)

    Google Scholar 

  57. 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)

    Article  MATH  Google Scholar 

  58. 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)

    Google Scholar 

  59. 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)

    Google Scholar 

  60. Gupta, J.K., Kumar, R.: An efficient ANN based approach for latent fingerprint matching. Int. J. Comput. Appl. 7(10), 18–21 (2010)

    Google Scholar 

  61. 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)

    Article  Google Scholar 

  62. Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)

    Article  Google Scholar 

  63. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)

    MATH  Google Scholar 

  64. 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)

    Google Scholar 

  65. Martinez-Munoz, G., Suárez, A.: Switching class labels to generate classification ensembles. Pattern Recognit. 38(10), 1483–1494 (2005)

    Article  Google Scholar 

  66. 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)

    Google Scholar 

  67. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2000)

    Google Scholar 

  68. Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)

    Article  Google Scholar 

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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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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Bindu Madhavi Padmanabhuni

  2. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Kartick Subramanian & Suresh Sundaram

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  1. Bindu Madhavi Padmanabhuni
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  2. Kartick Subramanian
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  3. Suresh Sundaram
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Correspondence to Bindu Madhavi Padmanabhuni .

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Editors and Affiliations

  1. Larus Technologies Corporation, Ottawa, Ontario, Canada

    Rami Abielmona

  2. Larus Technologies Corporation, Ottawa, Ontario, Canada

    Rafael Falcon

  3. Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada

    Nur Zincir-Heywood

  4. School of Engineering and Information Technology, University of New South Wales, Canberra, Aust Capital Terr, Australia

    Hussein A. Abbass

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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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  • DOI: https://doi.org/10.1007/978-3-319-26450-9_12

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