Abstract

Biometric systems identify a person through physical traits or verify his/her identity through automatic processes. Various systems were used over years including systems like fingerprint, iris, facial images, hand geometry and speaker recognition. For biometric systems successful implementation, it has to address issues like efficiency, accuracy, applicability, robustness and universality. Single modality based recognition verifications are not robust while combining information from different biometric modalities ensures better performance. Multimodal biometric systems use multiple biometrics and integrate information for identification. It compensates unimodal biometric systems limitations. This study considers multimodal biometrics based on fingerprint and finger veins. Gabor features are extracted from finger vein using Gabor filter with orientation of 0, 15, 45, 60 and 75°, respectively. For fingerprint images, energy coefficients are attained using wavelet packet tree. Both features are normalized using min max normalization and fused with concatenation. Feature selection is through PCA and kernel PCA. Classification is achieved through KNN, Naïve Bayes and RBF Neural Network Classifiers.

Keywords:

Biometric systems, Gabor filter, K-Nearest Neighbors (KNN), na, Principal Component Analysis (PCA),

References

1. Amiri, G.G. and A. Asadi, 2009. Comparison of different methods of wavelet and wavelet packet transform in processing ground motion records. Int. J. Civ. Eng., 7(4): 248-257.
   
2. Arivazhagan, S., T.G. Flora and L. Ganesan, 2007. Fingerprint verification using Gabor co-occurrence features. Proceeding of International Conference on Computational Intelligence and Multimedia Applications, 2: 281-285.
   CrossRef    
3. Beng, T.S. and B.A. Rosdi, 2011. Finger-vein identification using pattern map and principal component analysis. Proceeding of IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp: 530-534.
   CrossRef    
4. Choras, R.S., 2007. Image feature extraction techniques and their applications for CBIR and biometrics systems. Int. J. Bio. Biomed. Eng., 1(1): 6-16.
   
5. Conti, V., C. Militello, F. Sorbello and S. Vitabile, 2010. A frequency-based approach for features fusion in fingerprint and iris multimodal biometric identification systems. IEEE T. Syst. Man Cybernetics, Part C: Appli. Rev., 40(4): 384-395.
   
6. Dadgostar, M., P.R. Tabrizi, E. Fatemizadeh and H. Soltanian-Zadeh, 2009. Feature extraction using gabor-filter and recursive fisher linear discriminant with application in fingerprint identification. Proceeding of 7th International Conference on Advances in Pattern Recognition (ICAPR'09), pp: 217-220.
   CrossRef    
7. Darwish, A.A., W.M. Zaki, O.M. Saad, N.M. Nassar and G. Schaefer, 2010. Human authentication using face and fingerprint biometrics. Proceeding of 2nd International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN), pp: 274-278.
   CrossRef    
8. Derpanis, K.G., 2007. Gabor filter. York University.
   
9. Deshmukh, A., S. Pawar and M. Joshi, 2013. Feature level fusion of face and fingerprint modalities using Gabor filter bank. Proceeding of IEEE International Conference on Signal Processing, Computing and Control (ISPCC), pp: 1-5.
   CrossRef    
10. Elmir, Y., Z. Elberrichi and R. Adjoudj, 2012. Score level fusion based multimodal biometric identification (Fingerprint and voice). Proceeding of 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), pp: 146-150.
    CrossRef    
11. Ferrer, M.A., A. Morales, C.M. Travieso and J.B. Alonso, 2007. Low cost multimodal biometric identification system based on hand geometry, palm and finger print texture. Proceeding of 41st Annual IEEE International Carnahan Conference on Security Technology, pp: 52-58.
    CrossRef    
12. Fierrez-Aguilar, J., J. Ortega-Garcia, J. Gonzalez-Rodriguez and J. Bigun, 2005. Discriminative multimodal biometric authentication based on quality measures. Pattern Recogn., 38(5): 777-779.
    CrossRef    
13. Gargouri Ben Ayed, N., A.D. Masmoudi and D.S. Masmoudi, 2011. A new human identification based on fusion fingerprints and faces biometrics using LBP and GWN descriptors. Proceeding of 8th International Multi-Conference on Systems, Signals and Devices (SSD), pp: 1-7.
    
14. Guest, R. and O. Miguel-Hurtado, 2011. Enhancing off-line biometric signature verification using a fingerprint assessment approach. Proceeding of IEEE International Carnahan Conference on Security Technology (ICCST), pp: 1-4.
    CrossRef    
15. Honkela, A., S. Harmeling, L. Lundqvist and H. Valpola, 2004. Using kernel PCA for initialisation of variational Bayesian nonlinear blind source separation method. Proceedings of the 5th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2004) pp: 790-797.
    CrossRef    
16. Hwang, Y.S. and S.Y. Bang, 1997. An efficient method to construct a radial basis function neural network classifier. Neural Networks, 10(8): 1495-1503.
    CrossRef    
17. Indovina, M., U. Uludag, R. Snelick, A. Mink and A. Jain, 2003. Multimodal biometric authentication methods: a COTS approach. Proceeding of MMUA, pp: 99-106.
    
18. Jain, A. and C.K. Verma, 2012. A framework based on hybrid biometrics for personal verification systems. Int. J. Appl., 1(1): 55-58.
    
19. Jain, A.K., A. Ross and S. Prabhakar, 2004. An introduction to biometric recognition. IEEE T. Circuits Syst. Video Technol., 14: 4-20.
    CrossRef    
20. Jolliffe, I., 2005. Principal Component Analysis. John Wiley and Sons, Ltd, New York.
    CrossRef    
21. Kulkarni, S., G. Lugosi and S. Venkatesh, 1998. Learning pattern classification: A survey. IEEE T. Inform. Theor., 44(6).
    
22. McCallum, A. and K. Nigam, 1998. A comparison of event models for naive bayes text classification. Proceedings of AAAI-98 Workshop on Learning for Text Categorization, 752: 41-48.
    
23. Ross, A. and A. Jain, 2003. Information fusion in biometrics. Pattern Recogn. Lett., 24(13): 2115-2125.
    CrossRef    
24. Sangeetha, S. and N. Radha, 2013. A new framework for IRIS and fingerprint recognition using SVM classification and extreme learning machine based on score level fusion. Proceedings of 7th International Conference on Intelligent Systems and Control (ISCO), pp: 183-188.
    CrossRef    
25. Sasidhar, K., V.L. Kakulapati, K. Ramakrishna and K. KailasaRao, 2010. Multimodal biometric systems-study to improve accuracy and performance. arXiv Preprint arXiv: 1011.6220.
    
26. Shariatmadar, Z.S. and K. Faez, 2011. A novel approach for Finger-Knuckle-Print recognition based on Gabor feature fusion. Proceedings of 4th International Congress on Image and Signal Processing (CISP), 3: 1480-1484.
    CrossRef    
27. Shi, J.X. and X.F. Gu, 2010. The comparison of iris recognition using principal component analysis, independent component analysis and Gabor wavelets. Proceedings of 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), 1: 61-64.
    
28. Shinde, A.D., 2004. A wavelet packet based sifting process and its application for structural health monitoring. M.A. Thesis, Faculty of Worcester Polytechnic Institute.
    
29. Shukla, A., R. Tiwari and R. Kala, 2010. Multimodal biometric systems. Towards Hybrid and Adaptive Computing, pp: 401-418.
    CrossRef    
30. Snelick, R., U. Uludag, A. Mink, M. Indovina and A. Jain, 2005. Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems. IEEE T. Pattern Anal. Mach. Intell., 27(3): 450-455.
    CrossRef    PMid:15747798    
31. Timofeev, R., 2004. Classification and regression trees (cart) theory and applications. Humboldt University, Berlin.
    
32. Wayman, J., A. Jain, D. Maltoni and D. Maio, 2005. An introduction to biometric authentication systems. Biometric Syst., pp: 1-20.
    CrossRef    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.