http://iet.metastore.ingenta.com
1887

System for multimodal biometric recognition based on finger knuckle and finger vein using feature-level fusion and k-support vector machine classifier

System for multimodal biometric recognition based on finger knuckle and finger vein using feature-level fusion and k-support vector machine classifier

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In this study, the authors propose a multimodal biometric system by combining the finger knuckle and finger vein images at feature-level fusion using fractional firefly (FFF) optimisation. Biometric characteristics, like finger knuckle and finger vein are unique and secure. Initially, the features are extracted from the finger knuckle and finger vein images using repeated line tracking method. Then, a newly developed method of feature-level fusion using FFF optimisation is used. This method is utilised to find out the optimal weight score to fuse the extracted feature sets of finger knuckle and finger vein images. Thus, the recognition is carried out by the fused feature set using layered k-SVM (k-support vector machine) which is newly developed by combining the layered SVM classifier and k-neural network classifier. The experimental results are evaluated and the performance is analysed with false acceptance ratio, false rejection ratio and accuracy. The outcome of the proposed FFF optimisation system obtains a higher accuracy of 96%.

References

    1. 1)
      • 1. Jain, A.K., Hong, L., Kulkarni, Y.: ‘A multimodal biometric system using fingerprint, face and speech’. Proc. of Int. Conf. on Audio-and Video-based Biometric Person Authentication, 1999, pp. 182187.
    2. 2)
      • 2. Saini, R., Rana, N.: ‘Comparison of various biometric methods’, Adv. Sci. Technol., 2014, 2, (1), pp. 2430.
    3. 3)
      • 3. Perumal, E., Ramachandran, S.: ‘A multimodal biometric system based on palmprint and finger knuckle print recognition methods’, Inf. Technol., 2015, 12, (2), pp. 118127.
    4. 4)
      • 4. Neware, S., Mehta, K., Zadgaonkar, A.S.: ‘Finger knuckle surface biometrics’, Eng. Technol. Adv. Eng., 2012, 2, (12), pp. 452455.
    5. 5)
      • 5. Lu, L., Peng, J.: ‘Finger multi-biometric cryptosystem using feature-level fusion’, J. Signal Process., Image Process. Pattern Recogn., 2014, 7, (3), pp. 223236.
    6. 6)
      • 6. Kale, K.V., Rode, Y.S., Kazi, M.M., et al: ‘Multimodal biometric system using fingernail and finger knuckle’. Proc. of Int. Symp. on Computational and Business Intelligence, 2013, pp. 279283.
    7. 7)
      • 7. Jacob, A.J., Bhuvan, N.T., Thampi, S.M.: ‘Feature level fusion using multiple fingerprints’, Comput. Sci.-New Dimens. Perspect., 2011, 4(1), pp. 1318.
    8. 8)
      • 8. Kang, B.J., Park, K.R.: ‘Multimodal biometric method based on vein and geometry of a single finger’, IET Comput. Vis., 2010, 4, (3), pp. 209217.
    9. 9)
      • 9. Michael, G.K.O., Connie, T., Teoh, A.B.J.: ‘A contactless biometric system using multiple hand features’, Visual Commun. Image Represent., 2012, 23, pp. 10681084.
    10. 10)
      • 10. Ross, A., Govindarajan, R.: ‘Feature level fusion in biometric systems’. Proc. of Biometric Consortium Conf. (BCC), 2004.
    11. 11)
      • 11. Yang, W., Huang, X., Zhou, F., et al: ‘Comparative competitive coding for personal identification by using finger vein and finger dorsal texture fusion’, Inf. Sci., 2014, 268, pp. 2032.
    12. 12)
      • 12. Park, G., Kim, S.: ‘Hand biometric recognition based on fused hand geometry and vascular patterns’, Sensors, 2013, 13, pp. 28952910.
    13. 13)
      • 13. Rattani, A., Kisku, D.R., Bicego, M., et alFeature level fusion of face and fingerprint biometrics’. Proc. of Int. Conf. on BTAS, 2007, pp. 16.
    14. 14)
      • 14. Srivastava, D.K., Bhambhu, L.: ‘Data classification using support vector machine’, J. Theor. Appl. Inf. Technol., 2009, 12, (1), , pp. 17.
    15. 15)
      • 15. Dass, S.C., Nandakumar, K., Jain, A.K.: ‘A principled approach to score level fusion in multimodal biometric systems’. Proc. of Audio-and Video-Based Biometric Person Authentication, 2005, pp. 10491058.
    16. 16)
      • 16. Feifei, C.U.I., Gongping, Y.A.N.G.: ‘Score level fusion of fingerprint and finger vein recognition’, Comput. Inf. Syst., 2011, 7, (16), pp. 57235731.
    17. 17)
      • 17. Jain, A.K., Ross, A., Prabhakar, S.: ‘An introduction to biometric recognition’, Circuits Syst. Video Technol., 2004, 14, (1), pp. 420.
    18. 18)
      • 18. Yang, J., Zhang, X.: ‘Feature-level fusion of fingerprint and finger-vein for personal identification’, Pattern Recogn. Lett., 2012, 33, pp. 623628.
    19. 19)
      • 19. Park, Y.H., Tien, D.N., Lee, E.C., et al: ‘A multimodal biometric recognition of touched fingerprint and finger-vein’. Proc. of Int. Conf. on Multimedia and Signal Processing, 2011, vol. 1, pp. 247250.
    20. 20)
      • 20. Miura, N., Nagasaka, A., Miyatake, T.: ‘Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification’, Mach. Vis. Appl., 2004, 15, pp. 194203.
    21. 21)
      • 21. Kumar, A., Ravikanth, C.: ‘Personal authentication using finger knuckle surface’, IEEE Trans. Inf. Forensics Sec., 2009, 4, (1), pp. 98110.
    22. 22)
      • 22. Kumar, A., Zhou, Y.: ‘Human identification using finger images’, IEEE Trans. Image Process., 2012, 21, (4), pp. 22282244.
    23. 23)
      • 23. Miura, N., Nagasaka, A., Miyatake, T.: ‘Extraction of finger vein patterns using maximum curvature points in image profiles’, IEICE Trans. Inf. Syst., 2007, 8, pp. 11851194.
    24. 24)
      • 24. Yang, W., Yu, X., Liao, Q.: ‘Personal authentication using finger vein pattern and finger-dorsa texture fusion’. Proc. of the 17th ACM Int. Conf. on Multimedia, 2009, pp. 905908.
    25. 25)
      • 25. Prabhakar, S., Pankanti, S., Jain, A.K.: ‘Biometric recognition: security and privacy concerns’, IEEE Secur. Priv., 2003, 1, (2), pp. 3342.
    26. 26)
      • 26. Deepak, A., Shirsat, S.: ‘Multimodal biometric recognition system’. Proc. of Int. Conf. on recent Innovations in Engineering and Management, 2016, pp. 237244.
    27. 27)
      • 27. Yang, X.-S.: ‘Firefly algorithm, stochastic test functions and design optimisation’, Int. J. Bio-Inspired Comput., 2010, 2, (2), pp. 7884.
    28. 28)
      • 28. Solteiro Pires, E.J., Tenreiro Machado, J.A.: ‘Particle swarm optimization with fractional-order velocity’ (Springer, 2011), pp. 296301.
    29. 29)
      • 29. IIT Delhi Finger Knuckle Database from http://www4.comp.polyu.edu.hk/~csajaykr/IITD/iitd_knuckle.htm.
    30. 30)
      • 30. SDUMLA-HMT a finger vein database from http://mla.sdu.edu.cn/sdumla-hmt.html.
    31. 31)
      • 31. Zhang, L., Zhang, L., Zhang, D., et al: ‘Online finger-knuckle-print verification for personal authentication’, Pattern Recogn., 2010, 43, (7), pp. 25602571.
    32. 32)
      • 32. Yang, J., Li, X.: ‘Efficient finger vein localization and recognition’. 20th Int. Conf. on Pattern Recognition (ICPR), 2010, pp. 11481151.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2016.0112
Loading

Related content

content/journals/10.1049/iet-bmt.2016.0112
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address