Abstract
This paper outlines a novel personal authentication approach by integrating the multiple feature representations of thermal hand vein patterns. In the present work, vein patterns are regarded as comprising textures. Accordingly two types of texture features using Gabor wavelets and fuzzy logic are extracted from the acquired vein images. Since both the approaches have different domains of feature representation, their integration is accomplished at the decision level by incorporating individual decisions using the Euclidean distance based classifiers. The optimal decision parameters comprising individual decision thresholds and one fusion rule out of 16 rules for two features are estimated with the help of hybrid Particle Swarm Optimization (PSO) which can optimize the decisions taken by the individual classifiers. The experimental results carried out on 100 user database are promising thus confirming the usefulness of the proposed authentication system.
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References
Lin, C.-L., Fan, K.-C.: Biometric verification using thermal images of palm-dorsa vein patterns. IEEE transactions on circuits and systems for video technology 14, 199–213 (2004)
Wang, L., Leedham, G.: A Thermal Hand Vein Pattern Verification System. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3687, pp. 58–65. Springer, Heidelberg (2005)
Wang, L., Leedham, G., Cho, S.-Y.: Infrared imaging of hand vein patterns for biometric purposes. IET Compt. Vis. 1, 113–122 (2007)
Wang, L., Leedham, G.: Near- and Far- Infrared Imaging for Vein Pattern Biometrics. In: IEEE Int. Conf. on Video Based Surveillance International Conference (2006)
Malki, S., Spaanenburg, L.: Hand Veins Feature Extraction Using DT-CNNS. In: Proc. SPIE, vol. 6590 (2007)
Miura, N., Nagasaka, A., Miyatake, T.: Feature Extraction of Finger-Vein Pattern Based on Repeated Line Tracking and its application to personal Identification. Machine Vision and Applications 15, 194–203 (2004)
Wang, K., Zhang, Y., Yuan, Z., Zhuang, D.: Hand Vein Recognition Based on Multi Supplemental Features of Multi-Classifier Fusion Decision. In: Proceedings of the 2006 IEEE International Conference on Mechatronics and Automation, Luoyang, China (June 2006)
Kumar, A., Hanmandlu, M., Gupta, H.M.: Online Biometric Authentication Using Hand Vein Patterns. In: IEEE Symposium: Computational Intelligence for Security and Defense Applications, Ottawa, Canada, July 8-10, (2009)
Hanmandlu, M., Kumar, A., Madasu, V.K., Yarlagadda, P.: Fusion of Hand Based Biometrics using Particle Swarm optimization. In: Proc. of the Fifth Int. Conf. on Information Technology: New Generations, USA, pp. 783–788 (2008)
Veeramachaneni, K., Osadciw, L.A., Varshney, P.K.: An Adaptive Multimodal Biometric Management Algorithm. IEEE Trans. On Systems, Man,and Cybernetics—Part C: Applications And Reviews 35(3) (August 2005)
Eberhart, R.C., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In: Proceedings Sixth Symposium on Micro Machine and Human Science, pp. 39–43. IEEE Service Center, Piscataway (1995)
Passino, K.M.: Biomimicry of Bacteria Foraging for Distributed Optimization and control. IEEE Control Systems Magazine (June 2002)
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Kumar, A., Hanmandlu, M., Gupta, H.M. (2010). Hybrid PSO Based Integration of Multiple Representations of Thermal Hand Vein Patterns. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_30
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DOI: https://doi.org/10.1007/978-3-642-17563-3_30
Publisher Name: Springer, Berlin, Heidelberg
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