Skip to main content
SpringerLink
Log in

An Effective Security Based Wireless Information System Based on Fusion Principal Component Analysis

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In the real world applications, wireless networks are an integral part of day-to-day life for many people, with businesses and home users relying on them for connectivity and communication. This paper examines the problems relating to the topic of wireless security and the background literature. The biometric systems often face limitations because of sensitivity to noise, intra class invariability, data quality, and other factors. Improving the performance of individual matchers in the aforementioned situation may not be effective. Multi biometric systems are used to overcome this problem by providing multiple pieces of evidence of the same identity. This system provides effective fusion scheme that combines information presented by the multiple domain experts based on the Rank level fusion integration method, thereby increasing the efficiency of the system which is not possible by the unimodal biometric system. The proposed multimodal biometric system has a number of unique qualities, starting from utilizing principal component analysis and fisher’s linear discriminant methods for individual matchers authentication and the novel rank level fusion method is used in order to consolidate the results obtained from different biometric matchers. The ranks of the individual matchers are combined using highest rank, Borda count, and logistic regression method. From the results it can be concluded that the overall performances of the wireless security based multi biometric systems are improved even in the presence of quality of data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Monwar, M. M., & Gavrilova, M. L. (2009). Multimodal biometric system using Rank Level Fusion approach. IEEE Transactions on System, Man and Cybernetics – Part B: Cybernetics, 39(4), 67–73.

  2. Chang, K., Bower, K. W., Sarkar, S., & Victor, B. (2003). Comparison and combination of ear and face images in appearance-based biometrics. IEEE Transactions Pattern Analysis and Machine Intelligence, 25(9), 1160–1165.

    Article  Google Scholar 

  3. Snelick, R., Uludag, U., Mink, A., Indovina, M., & Jain, A. (2005). Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(3), 450–455.

    Article  Google Scholar 

  4. Toh, K. A., Jiang, X. D., & Yau, W. Y. (2004). Exploiting global and local decisions for multi-modal biometrics verification. IEEE Transaction on Signal Processing, 52(10), 3059–3072.

    Article  Google Scholar 

  5. Ross, A., Nandakumar, K., & Jain, A. K. (2006). Handbook of multibiometrics. New York: Springer.

    Google Scholar 

  6. Hong, L., & Jain, A. K. (1998). Integrating faces and fingerprints for personal identification. IEEE Transaction on Pattern Analysis Machine Intelligence, 20(12), 1295–1307.

    Article  Google Scholar 

  7. Marcialis, L., & Roli, F. (2004). Fingerprint verification by fusion of optical and capacitive sensors. Pattern Recognition Letters, 25(11), 1315–1322.

    Article  Google Scholar 

  8. Frischholz, R., & Dieckmann, U. (2000). BiolD: A multimodal biometric identification system. Computer Science Journal, 33(2), 64–68.

    Google Scholar 

  9. Kumar, A., & Zhang, D. (2004). Palm print authentication using multiple classifiers. In Proceedings SPIE conference, biometric technology human identification, Orlando (pp. 20–29).

  10. Heseltine, T., Pears, N., Austin, J., & Chen, Z.(2003). Face recognition: A comparison of appearance-based approaches. In Proceedings 7th digital image computing: Techniques and applications, Sydey, Australia (pp. 59–68).

  11. Rodrigues, R. N., Ling, L. L., & Govindaraju, V. (2009). Robustness of multi-modal biometric fusion methods against spoof attacks. Journal of Visual Language and Computing, 20, 169–179.

    Article  Google Scholar 

  12. Akhtar, Z. (2012). Security of multimodal biometric systems against spoof attacks. Ph.D. Thesis, University of Cagliari, Italy.

  13. Akhtar, Z., & Alfarid, N. (2011). Secure learning algorithm for multimodal biometric systems against spoof attacks. In International conference on information and network technology (ICINT) (pp. 52–57).

  14. Rahman, M. M., & Ishikawa, S. (2004). A robust recognition method for partially occluded/destroyed objects. In Proceedings 6th Asian conference on computer vision, Jeju, Korea (pp. 984–988).

  15. Fierrez-Aguilar, J., Ortega-Garcia, J., Garcia-Romero, D., & Gonzalez- Rodriguez, J. (2003). A comparative evaluation of fusion strategies for multimodal biometric verification. In Proceedings of 4th international conference on audio–video-based biometric person authentication (Vol. LNCS 2688, pp. 830–837).

  16. Wang, T., Tan, T., & Jain, A. K. (2003). Combining face and iris biometrics for identity verification. In Proceedings 4th international conference on audio–video-based biometric person authentication (Vol. LNCS 2688, pp. 805–813).

  17. Ross, A., & Jain, A. K. (2003). Information fusion in biometrics. Pattern Recognition Letters, 24(13), 2115–2125.

    Article  Google Scholar 

  18. Snelick, R., Uludag, U., Mink, A., Indovina, M., & Jain, A. K. (2005). Large scale evaluation of multimodal biometric authentication using state-of the-art systems. IEEE Transaction Pattern Analysis and Machine Intelligence, 27(3), 450–455.

    Article  Google Scholar 

  19. Erp, M. V., & Schomaker, L. (2000). Variants of the Borda count method for combining ranked classifier hypotheses. In Proceedings 7th international workshop frontiers handwriting recognition, Amsterdam, The Netherlands (pp. 443–452).

  20. Kinnunen, T., Hautamäki, V., & Fränti, P. (2004). Fusion of spectral feature sets for accurate speaker identification. In Proceedings 9th conference speech and computer, St. Petersburg, Russia (pp. 361–365).

  21. Bhatnagar, J., Kumar, A., & Saggar, N. (2007). A novel approach to improve biometric recognition using rank level fusion. In Proceedings IEEE conference computing visual pattern recognition (pp. 1–6).

  22. Marcialis, G. L., & Roli, F. (2004). Fusion of appearance-based face recognition algorithms. Pattern Analysis Application, 7(2), 151–163.

    Article  MathSciNet  Google Scholar 

  23. Down, M. P., & Sands, R. J. (2004). Biometrics: An overview of the technology, challenges and control considerations. Information System Control Journal, 4, 53–56.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ECE Department, Sri Ramakrishna Institute of Technology, Coimbatore, 641010, India

    S. Mary Praveena

  2. EEE Department, PSG College of Technology, Coimbatore, 641004, India

    Ila. Vennila

Authors
  1. S. Mary Praveena
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ila. Vennila
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Mary Praveena.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mary Praveena, S., Vennila, I. An Effective Security Based Wireless Information System Based on Fusion Principal Component Analysis. Wireless Pers Commun 86, 887–899 (2016). https://doi.org/10.1007/s11277-015-2960-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-015-2960-7

Keywords

Search

Navigation