Abstract
Reliable personal authentication system is essential for social, financial and political structures of today’s human life style. The advent of biometric technology has revolutionized personal authentication system to meet the current requirements through biometric modalities in a reliable, accurate, rapid and user-friendly way. However, there exist a number of unresolved issues for the biometric systems related to data, system design and algorithms. This work focuses on exploring features from dorsal side of the hand region known as finger knuckle surface for reliable personal authentication. This paper illustrates design and development of an integrated finger knuckle biometric framework using multiple units of finger knuckle surface and multi-algorithmic parameters for robust and accurate personal identification. This novel integrated approach known as Multi-Faceted and Multi-Algorithmic Framework (MFMA) for authentication using finger knuckle surface. This MFMA framework simultaneously acquires multiple instances of finger back knuckle surface, extracts multiple features using three different categories of algorithms, viz., angular geometric analysis, transform based texture analysis, statistical analysis and integrates the information derived from multiple algorithms using decision level fusion implemented based on Bayesian approach.
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References
S. Nanavati, M. Thieme, N. Raj, and R. Nanavati, Biometrics: Identity Verification in a Networked World. John Wiley and Sons, 2002.
J. L. Wayman, A. K. Jain, D. Maltoni, and D. Maio, Biometric Systems: Technology, Design and Performance Evaluation, 1st ed. Springer Publishing Company, 2010.
A. K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Transactions on Circuits and System for Video Technology, vol. 14, no. 1, pp. 4–20, Jan. 2004.
A. K. Jain, P. Flynn, and A. Ross, Handbook of Biometrics. Springer, New York, 2007.
S. Kung, M. Mak, and S. Lin, Biometric Authentication: A Machine Learning Approach, 1st ed. Prentice Hall Press, 2004.
D. Zhang, Automated Biometrics: Technologies and Systems, 1st ed. Springer Publishing Company, 2013.
G. Michael, T. Connie, T. Chin, N. Foon, and A. Jin, “Realizing hand-based biometrics based on visible and infrared imagery,” in Proceedings of Neural Information Processing Models and Applications. Springer Berlin Heidelberg, vol. 6444, no. 4, pp. 606–615, Mar. 2010.
A. K. Jain and D. Maltoni, Handbook of Fingerprint Recognition. Springer, New York, 2003.
G. Lu, D. Zhang, and K. Wang, “Palmprint recognition using eigenpalms features,” Pattern Recognition Letters, vol. 24, no. 9–10, pp. 1463–1467, June 2003.
A. Ross, “A prototype hand geometry-based verification system,” in Proceedings of Audio and Video based Biometric Person Authentication, vol. 3, no. 1, pp. 166–171, Sep. 1999.
P. Gupta and P. Gupta, “Extraction of true palm-dorsa veins for human authentication,” in Proceedings of the Indian Conference on Computer Vision Graphics and Image Processing, vol. 4, no. 1, pp. 351–358, May 2014.
L. Zhang, D. Zhang, and D. Zhang, “Finger-knuckle-print: A new biometric identifier,” in Proceedings of 16th IEEE International Conference on Image Processing, vol. 3, no. 1, pp. 1981–1984, Nov. 2009.
A. Kumar and Y. Zhou, “Personal identification using finger knuckle orientation features,” Electronics Letters, vol. 45, no. 20, pp. 1023–1025, Sep. 2009.
M. Xiong, W. Yang, and C. Sun, “Finger-knuckle-print recognition using LGBP,” in Advances in Neural Networks, ser. Lecture Notes in Computer Science. Springer Berlin Heidelberg, vol. 6676, no. 3, pp. 270–277, Oct. 2011.
K. Usha and M. Ezhilarasan, “Finger Knuckle Biometrics—A Review,” Computer and Electrical Engineering, vol. 45, no.1, 249–259, 2015.
D. L. Woodard and P. J. Flynn, “Finger surface as a biometric identifier,” Computer Vision and Image Understanding, vol. 100, no. 3, pp. 357–384, Dec. 2005
Jialiang Penga, Ahmed A. Abd El-Latifb, Qiong Lic, Xiamu Niu, “Multimodal biometric authentication based on score level fusion of finger biometrics,” Optik Vol. 125 no. 1, pp. 6891–6897, 2014
A. Kumar and C. Ravikanth, “Personal authentication using finger knuckle surface,” IEEE Transactions on Information Forensics and Security, vol. 4, no. 1, pp. 98–110, Mar. 2009.
Aditya Nigam and Phalguni Gupta, “Designing an accurate hand biometric based authentication system fusing finger knuckle print and palm print”, Neurocomputing, vol. 151, pp. 1120–1132, 2015.
K. Usha and M. Ezhilarasan, “Hough Transform based Feature Extraction Algorithm for Finger Knuckle Biometric Recognition System,” in Proceedings of International Conference of Advanced Computing, Networking and Informatics, Bhubaneswar, India, Springer, vol. 1, no. 27, pp. 463–472, June 2014.
Usha K and Ezhilarasan M, “Robust personal authentication using finger knuckle geometric and texture features,” Ain Shams Engineering Journal, (2016), https://doi.org/10.1016/j.asej.2016.04.006.
Usha K and Ezhilarasan, “Personal Recognition using Finger Knuckle Shape Oriented Features and Texture Analysis,” Journal of King Saud University—Computer and Information Sciences, (2015), https://doi.org/10.1016/j.jksuci.2015.02.004.
K. Usha and M. Ezhilarasan, “Personal Authentication based on Angular Geometric Analysis using Finger Back Knuckle Surface,” in Proceedings of 3rd International Conference on Advances in Communication Network and Computing, Chennai, India, Elsevier, vol. 1, no. 1, pp. 51–58, January 2013.
K. Usha and M. Ezhilarasan, “Hybrid Detection of Convex Curves for Biometric Authentication using Tangents and Secants,” in Proceedings of 3rd IEEE International Advance Computing Conference, Ghazhiabad, India, vol. 1, no. 1, pp. 756–761, January 2013.
K. Usha and M. Ezhilarasan, “Fusion of geometric and texture features for finger knuckle surface recognition,” Alexandria Engineering Journal, vol. 55, no. 1, pp. 683–697, 2016.
N. Poh and J. Kittler, “A unified framework for biometric expert fusion incorporating quality measures,” IEEE Transactions Analysis and Machine Intelligence, vol. 34, no. 1, pp. 3–18, Jan. 2012.
K. Usha and M. Ezhilarasan, “A Competent Method for Personal Authentication based on Intra-Knuckle Parameters,” in Proceedings of 3rd Third International Conference on Advances in Information Technology and Mobile Communication, Bangalore, India, Elsevier, vol. 1, no. 1, pp. 269–276, April 2013.
R. M. Bolle, S. Pankanti, and N. K. Ratha, “Evaluation techniques for biometrics-based authentication systems,” in Proceedings of 15th International Conference on Pattern Recognition, vol. 3, no. 2, pp. 831–837, Sep. 2000.
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Usha, K., Thenmozhi, T., Ezhilalarasan, M. (2020). Multi-faceted and Multi-algorithmic Framework (MFMA) for Finger Knuckle Biometrics. In: Smys, S., Iliyasu, A.M., Bestak, R., Shi, F. (eds) New Trends in Computational Vision and Bio-inspired Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-41862-5_172
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DOI: https://doi.org/10.1007/978-3-030-41862-5_172
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