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Biometric recognition by hybridization of evolutionary fuzzy clustering with functional neural networks

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Abstract

Computational intelligence is an emerging area having caliber to solve many real world complex problems. Proper synergism of evolutionary, fuzzy and neural techniques can be more suitable for solving these problems. A novel approach for human recognition which is based on the fusion of evolutionary fuzzy clustering and functional modular neural networks (FMNN) is presented in this paper. Here, evolutionary fuzzy clustering with Minkowski distance (EFC-MD) is proposed for pre-classification task which allocates training patterns into optimal number of clusters. Considering Minkowski distance matrices instead of Euclidian distance provide flexibility to clustering algorithm in acquiring any shapes for clusters. The functional modular neural network is trained according to the fuzzy distribution of patterns in a cluster by EFC-MD. The functional neural network discriminates itself with the conventional neural network in the context it process and classifies the patterns on the basis of fuzzy distribution of training patterns. Final recognition or identification of patterns is based on combined outcomes of FMNN. The experimental results present the efficacy of proposed technique and compare it with the recent research outcomes in related areas. The motivation of this fusion is demonstrated through four benchmark biometric datasets.

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References

  • Avila S, Sanchez R, Roche D (2001) Iris recognition for biometric identification using dyadic wavelet transform zero-crossing. In: Proceedings of the IEEE 35th International. Camahan Conference on Security Technology, pp 272–277

  • Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigen faces versus fisher faces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19:711–720

    Google Scholar 

  • Bevilacqua V, Cariello L, Carro G, Daleno D, Mastronardi G (2008) A face recognition system based on pseudo 2D HMM applied to neural network coefficient. Soft Comput 12:615–621

    Article  Google Scholar 

  • Bhattacharjee D, Basu DK, Nasipuri M, Kundu M (2010) Human face recognition using fuzzy multilayer perceptron. Soft Comput 14:559–570

    Google Scholar 

  • Daughman J (2004) How iris recognition works. IEEE Trans Circ Syst Video Technol 14(1):21–30

    Article  Google Scholar 

  • Daugman JG (2001) Statistical richness of visual phase information: update on recognizing persons by iris patterns. Int J Comput Vision 45(1):25–38

    Article  MATH  Google Scholar 

  • Eickeler S, Mueller S, Rigoll G (1999) High quality face recognition in JPEG compressed images. In: Proceedings IEEE International Conference Image Processing, pp 672–676

  • Er MJ, Wu S, Lu J, Toh HL (2002) Face recognition with radial basis function RBF neural network. IEEE Trans Neural Netw 13(3):697–710

    Article  Google Scholar 

  • Farooq A (2002) Biologically inspired modular neural networks. Dissertation, Virginia Technology

    Google Scholar 

  • Gaxiola F, Melin P (2010) Modular neural networks for person recognition using segmentation and the iris biometric measurement with image preprocessing. In: International Joint Conference on Neural Networks (IJCNN), pp 2765–2771

  • Groenen PJF, Jajuga K (2001) Fuzzy clustering with squared Minkowski distances. Fuzzy Sets Syst 120:227–237

    Google Scholar 

  • Handl J, Knowles J (2007) An evolutionary approach to multiobjective clustering. IEEE Trans Evol Comput 11(1):56–76

    Article  Google Scholar 

  • Holappa J, Ahonen T, Pietikainen M (2008) An optimized illumination normalization method for face recognition. In: Proceedings of IEEE International Conference Biometrics: Theory, Application and System, pp 1–6

  • Hruschka ER, Campello RJGB, Freitas AA, de Carvelho ACPLF (2009) A survey of evolutionary algorithms for clustering. IEEE Trans System Man Cybernet Part C: Appl Rev 39(2):133–155

    Article  Google Scholar 

  • Jain AK, Ross A, Pankanti S (2006) Biometrics: a tool for information security. IEEE Tran Inform Forensics Secur 1(2):125–143

    Article  Google Scholar 

  • Komogortsev O, Karpov A, Price L, Aragon L (2012) Biometric authentication via oculomotor plant characteristics. In: Proceedings of IEEE International Conference on Biometrics, pp 1–8

  • Li SJ, Lu J (1999) Face recognition using nearest feature line method. IEEE Trans Neural Netw 10(2):439–445

    Article  Google Scholar 

  • Liu C (2006) Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance. IEEE Trans Pattern Anal Mach Intell 28(5):725–737

    Article  Google Scholar 

  • Lu J, Plataniotis KN, Venetsanopoulos AN (2003) Face recognition using LDA based algorithms. IEEE Trans Neural Netw 14(1):195–200

    Article  Google Scholar 

  • Ma, L et al. (2002) Iris recognition using circular symmetric filters. In: Proceedings of the 16th International Conference on Pattern Recognition, vol 2, pp 414–417

  • Mingoti SA, Lima JO (2006) Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms. Eur J Oper Res 174:1742–1759

    Article  MATH  Google Scholar 

  • Ojansivu V, Rahtu E, Heikkilä J (2008) Rotation invariant local phase quantization for blur insensitive texture analysis. In: Proceedings of IEEE International Conference Pattern Recognition, pp 1–4

  • Ozbey Y, Ceylan R, Karlik B (2006) A fuzzy clustering neural network architecture for classification of ECG arrhythmias. Comput Biol Med 36:376–388

    Google Scholar 

  • Park U, Jillala RR, Ross A, Jain A (2011) Periocular biometrics in visible spectrum. IEEE Trans Inform Forensics Security 6(1):96–106

    Article  Google Scholar 

  • Phillips PJ (1998) Matching pursuit filters applied to face identifications. IEEE Trans Image Process 7:1150–1164

    Article  Google Scholar 

  • Phillips PJ, Flynn PJ, Scruggs T, Bowyer KW, Chang J, Hoffman K, Marques J, Min J, Worek W (2005) Overview of the face recognition grand challenge. In: Proceedings of IEEE Conference Computer Vision Pattern Recognition, pp 947–954

  • Proenc¸a H, Alexandre L (2005) UBIRIS: A noisy iris image database. In: Proceedings of 13th International Conference Image Analysis and Processing, pp 970–977. http://iris.di.ubi.pt

  • Proenc¸a H, Alexandre L (2007) Towards non co-operative iris recognition: a classification approach using multiple signature. IEEE Trans Pattern Anal Mach Intell 9(4):607–612

    Article  Google Scholar 

  • Shih P, Liu C (2005) Evolving effective color features for improving FRGC baseline performance. In: Proceedings of Computer Vision Pattern Recognition Workshop, pp 156–163

  • Sirlantzis K, Hoque S, Fairhurst MC (2008) Diversity in multiple classifier ensembles based on binary feature quantisation with application to face recognition. Appl Soft Comput 8(1):437–445

    Article  Google Scholar 

  • Tan T, Yan H (2000) Object recognition using fractal neighbor distance: Eventual convergence and recognition rates. In: Proceedings of 15th International Conference Pattern Recognition, pp 781–784

  • Tisse CL, Torres L, Robert M (2002) Person identification technique using human iris recognition. In: Proceedings of the 15th International Conference on Vision Interfacem, pp 294–299

  • Tolba AS, Abu-Rezq AN (1999) Combined classifiers for invariant face recognition. In: Proceedings International Conference Information Intelligence System, pp 350–359

  • Torre FD, Gross R, Baker S, Kumar V (2005) Representational oriented component analysis (ROCA) for face recognition with one sample image per training class. In: Proceedings IEEE Conference Computer Vision Pattern Recognition, pp 266–273

  • Tripathi BK, Kalra PK (2010) The novel aggregation function based neuron models in complex domain. Soft Comput 14(10):1069–1081

    Google Scholar 

  • Tripathi BK, Kalra PK (2011) On efficient learning machine with root-power mean neuron in complex-domain. IEEE Trans Neural Netw 22(5):727–738

    Article  Google Scholar 

  • Tseng LY, Yang SB (2001) A genetic approach to the automatic clustering problem. Pattern Recogn 34:415–424

    Article  MATH  Google Scholar 

  • Vatsa M, Singh R, Gupta P (2004) Comparison of iris recognition algorithm. In: IEEE Internationl Conference of Intelligent Sensing and Information Processing, pp 354–358

  • Vitabile S, Conti V, Collotta M, Scatà G, Andolina S, Gentile A, Sorbello F (2012) A real-time network architecture for biometric data delivery in ambient intelligence. Ambient Intell Humanized Comput. doi:10.1007/s12652-011-0104-9

    Google Scholar 

  • Yu J, Cheng Q, Hung H (2004) Analysis of weighting exponent in the FCM. IEEE Trans Syst Man Cybern B 34(1): 634–638

    Google Scholar 

  • Yuan B, Klir GJ, Stone JF (1995) Evolutionary fuzzy c-mean clustering. In: Proceedings of International Conference on Fuzzy System, pp 2221–2226

  • Zhao W, Chellappa R, Rosenfeld A, Phillips PJ (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–458

    Article  Google Scholar 

  • Zuo W, Wang K, Zhang D, Zhang H (2007) Combination of two novel LDA-based methods for face recognition. Neurocomputing 70(4–6):735–742

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Harcourt Butler Technological Institute, Kanpur, India

    Vivek Srivastava, Bipin Kumar Tripathi & Vinay Kumar Pathak

Authors
  1. Vivek Srivastava
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  2. Bipin Kumar Tripathi
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  3. Vinay Kumar Pathak
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Correspondence to Vivek Srivastava.

Additional information

B. K. Tripathi associated with computational intelligence research group of Indian Institute of Technology, Kanpur, India.

V. K. Pathak associated with biometric research team of Indian Institute of Technology, Kanpur, India.

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Srivastava, V., Tripathi, B.K. & Pathak, V.K. Biometric recognition by hybridization of evolutionary fuzzy clustering with functional neural networks. J Ambient Intell Human Comput 5, 525–537 (2014). https://doi.org/10.1007/s12652-012-0161-8

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  • DOI: https://doi.org/10.1007/s12652-012-0161-8

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