Selective Small Reconstruction Error Based LDRC Multimodal Biometric Authentication

Savitha G.¹ , Vibha L.² , Venugopal K. R.³

1. Dept. Computer Science and Engineering, B.N.M. Institute of Technology, Bangalore, India.
2. Dept. Computer Science and Engineering, B.N.M. Institute of Technology, of University, Bangalore, India.
3. Dept. Computer Science and Engineering, Visvesvaraya College of Engineering, Bangalore, India.

Correspondence should be addressed to: savithaphd2016@gmail.com.

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.1 , pp.1-10, Feb-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i1.110

Online published on Feb 28, 2018

Copyright © Savitha G., Vibha L., Venugopal K. R. . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Biometric authentication has attracted great interest due to its importance in numerous real-world applications. In this paper, the accuracy issue addressed through multimodal biometric combination. The proposed multimodal biometric combination scheme delivers face, finger print and signature as biometric characteristics, as an input for security purpose. The proposed methodology incorporates Wiener filter for preprocessing the acquired images and Discrete Wavelet Transform (DWT) was used for achieving feature subsets. Then, Linear Discriminant Regression Classification (LDRC) was designed with the combination of Selective Small Reconstruction Error (SSRE), which helps to select the appropriate classes. In experimental analysis, the proposed approach improves the authentication rate by means of False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER). The experimental outcome shows that the proposed methodology improved accuracy in biometric authentication rate up to 5-10% compared to the existing method: Linear Regression Classification (LRC).

Key-Words / Index Term :
Biometric Authentication; Discrete Wavelet Transform; Selective Small Reconstruction Error; Wiener Filter

References :
[1] A.K. Jain, A. Ross, S. Prabhakar, “An introduction to biometric recognition”, IEEE Transactions on Circuits and Systems for Video Technology, Vol.14, No.1, pp.4-20, 2004.
[2] A.K. Jain, A. Ross, “Multibiometric systems”, Communications of the ACM, Vol.47, No.1, pp.34-40, 2004.
[3] R. Raghavendra, B. Dorizzi, A. Rao, G.H. Kumar, “Designing efficient fusion schemes for multimodal biometric systems using face and palmprint”, Pattern Recognition, Vol.44, No.5, pp.1076-1088, 2011.
[4] N. Poh, J. Kittler, “A unified framework for biometric expert fusion incorporating quality measures”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.34, no.1, pp.3-18, 2012.
[5] M. Hofmann, S.M. Schmidt, A.N. Rajagopalan, G. Rigoll, “Combined face and gait recognition using alpha matte preprocessing”, In the Proceedings of 5th IAPR International Conference on Biometrics (ICB), pp.390-395, 2012.
[6] U. Uludag, S. Pankanti, S. Prabhakar, A.K. Jain, “Biometric cryptosystems: issues and challenges”, Proceedings of the IEEE, Vol.92, No.6, pp.948-960, 2004.
[7] W.J. Scheirer, T.E. Boult, “Bio-cryptographic protocols with bipartite biotokens,” In the Proceedings of Biometrics Symposium, pp.9-16, 2008.
[8] A. Mishra, “Multimodal biometrics it is: need for future systems”, International journal of computer applications, Vol.3, No.4, pp.28-33, 2010.
[9] A. Ross, A.K. Jain, “Multimodal biometrics: An overview”, In the Proceedings of 12th European Conference In Signal Processing, pp.1221-1224, 2004.
[10] S. Sheena, S. Mathu, “A Study of Multimodal Biometric System”, IJRET: International Journal of Research in Engineering and Technology, Vol.3, No.14, pp.93-97, 2014.
[11] K. Sasidhar, V.L. Kakulapati, K. Ramakrishna, K. KailasaRao, “Multimodal biometric systems-Study to improve accuracy and performance”, International Journal of Computer Science & Engineering Survey (IJCSES), Vol.1, No.2, 2010.
[12] M. Soltane, M. Bakhti, “Multi-modal biometric authentications: concept issues and applications strategies”, International Journal of Advanced Science and Technology, Vol.48, 2012.
[13] M. Pathak, N. Srinivasu, “Analysis of Multimodal Biometric System Based on Level of Fusion”, International Journal of Inventive Engineering and Sciences (IJIES), Vol.3, no.8, 2015.
[14] Y. Li, M. Shi, E. Zhu, J. Yin, J. Zhao, “A Multimodal Fusion Algorithm Based on FRR and FAR Using SVM”, International Journal of Security and Its Applications, Vol.7, No.3, pp.65-74, 2013.
[15] G. Savitha, L. Vibha, K.R. Venugopal, “Multimodal Biometric Authentication System Using Ldr Based on Selective Small Reconstruction Error”, Journal of Theoretical and Applied Information Technology, Vol.92, No.1, pp.171, 2016.
[16] A. Muthukumar, C. Kasthuri, S. Kannan, “Multimodal Biometric Authentication using Particle Swarm Optimization Algorithm with Fingerprint And Iris”, ICTACT Journal on Image and Video Processing, Vol.2, No.3, 2012.
[17] Y. Lu, X. Fang, B. Xie, “Kernel linear regression for face recognition”, Neural Computing and Applications, Vol.24, No.7-8, pp.1843-1849, 2014.
[18] S. M. Huang, J. F. Yang, “Linear Discriminant Regression Classification for Face Recognition”, IEEE Transactions On Signal Processing Letters, Vol.20, No.1, 2013.
[19] B. Ma, Y. Wang, C. Li, Z. Zhang, D. Huang, “Secure multimodal biometric authentication with wavelet quantization based fingerprint watermarking”, Multimedia tools and applications, Vol.72, No.1, pp.637-666, 2014.
[20] H. Saevanee, N. Clarke, S. Furnell, V. Biscione, “Continuous user authentication using multi-modal biometrics”, Computers & Security, Vol.53, pp.234-246, 2015.
[21] A. Gupta, E. Sharma, N. Sachan, N. Tiwari, “Door Lock System through Face Recognition Using MATLAB”, International Journal of Scientific Research in Computer Science and Engineering, Vol.1, No.3, pp.51-55, 2013