A Comparative Study on Different Biometric Modals Using PCA

  • G. Pranay Kumar
  • Harendra Kumar Ram
  • Naushad Ali
  • Ritu Tiwari
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)


Multimodal Biometrics is a rising domain in biometric technology where more than one biometric trait is combined to improve the performance. It is well proved that multimodal biometrics has much higher efficiency than unimodal biometrics. In this research, a comparative study of multimodal biometrics has been done with four biometrics has been considered, three static, one behavioral namely face, ear, palm and gait respectively. The paper mainly focuses on four cases unimodal, bimodal, 3-modal, 4-modal biometrics and using PCA as the base feature extraction method in all cases. The training and testing algorithm has been embedded in PCA itself. the results of all cases are compared and that multimodal cases has much higher efficiency than unimodal and then comparing multimodal cases i.e. bi modal,3- modal,4-modal, there was not much difference and shows that, increasing the number of biometrics doesn’t make much difference.


Multimodal biometric PCA Feature extraction Face Recognition Ear Recognition Palm Recognition Gait Recognition 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 4–20 (2004)CrossRefGoogle Scholar
  2. 2.
    Jain, A.K., Bolle, R., Pankanti, S. (eds.): Biometrics: Personal Identification in Networked Society. Kluwer Academic Publishers (1999)Google Scholar
  3. 3.
    Yazdanpanah, A.P., Faez, K., Amirfattahi, R.: Multimodal biometric system using face, ear and gait biometrics. In: 2010 10th International Conference on Information Sciences Signal Processing and their Applications (ISSPA), May 10-13, pp. 251–254 (2010)Google Scholar
  4. 4.
    Kim, K.: Face Recognition using Principle Component Analysis. In: International Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1996)Google Scholar
  5. 5.
    Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1991, June 3-6, pp. 586–591 (1991)Google Scholar
  6. 6.
    Umbaugh, S.E., Wei, Y.-S., Zuke, M.: Feature extraction in image analysis. A program for facilitating data reduction in medical image classification. IEEE Engineering in Medicine and Biology Magazine 16(4), 62–73 (1997)CrossRefGoogle Scholar
  7. 7.
    Zhao, G., Liu, J.: Application of Principal Component Analysis and Neural Network on the Information System Evaluation. In: Pacific-Asia Conference on Circuits, Communications and Systems, PACCS 1909, May 16-17, pp. 785–788 (2009)Google Scholar
  8. 8.
    Delac, K., Grgic, M.: A survey of biometric recognition methods. In: 46th International Symposium Electronics in Marine, ELMAR 2004, June 16-18 (2004)Google Scholar
  9. 9.
    Ahmad, M.I., Woo, W.L., Dlay, S.S.: Multimodal biometric fusion at feature level: Face and palmprint. In: 2010 7th International Symposium on Communication Systems Networks and Digital Signal Processing (CSNDSP), July 21-23, pp. 801–805 (2010)Google Scholar
  10. 10.
    Adhinagara, Y., Tjokorda Agung, B.W., Retno, N.D.: Implementation of multimodal biometrics recognition system combined palm print and palm geometry features. In: 2011 International Conference on Electrical Engineering and Informatics (ICEEI), July 17-19, pp. 1–5 (2011)Google Scholar
  11. 11.
    Meraoumia, A., Chitroub, S., Bouridane, A.: Fusion of multispectral palmprint images for automatic person identification. In: 2011 Saudi International on Electronics, Communications and Photonics Conference (SIECPC), April 24-26, pp. 1–6 (2011)Google Scholar
  12. 12.
    Guo, Z., Zhang, L., Zhang, D.: Feature Band Selection for Multispectral Palmprint Recognition. In: 2010 20th International Conference on Pattern Recognition (ICPR), August 23-26, pp. 1136–1139 (2010)Google Scholar
  13. 13.
    Bozorgtabar, B., Noorian, F., Rad, G.A.R.: Comparison of different PCA based Face Recognition algorithms using Genetic Programming. In: 2010 5th International Symposium on Telecommunications (IST), December 4-6, pp. 801–805 (2010)Google Scholar
  14. 14.
    Abaza, A., Ross, A.: Towards understanding the symmetry of human ears: A biometric perspective. In: 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), September 27-29, pp. 1–7 (2010)Google Scholar
  15. 15.
    Cadavid, S., Mahoor, M.H., Abdel-Mottaleb, M.: Multimodal biometric modeling and recognition of the human face and ear. In: 2009 IEEE International Workshop on Safety, Security & Rescue Robotics (SSRR), November 3-6, pp. 1–6 (2009)Google Scholar
  16. 16.
    Connie, T., Teoh, A., Goh, M., Ngo, D.: Palmprint Recognition with PCA and ICA (November),
  17. 17.
    Xu, S.-L., Zhang, Q.-J.: Gait Recognition Using Fuzzy Principal Component Analysis. In: 2010 2nd International Conference on E-Business and Information System Security (EBISS), May 22-23, pp. 1–4 (2010)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • G. Pranay Kumar
    • 1
  • Harendra Kumar Ram
    • 1
  • Naushad Ali
    • 1
  • Ritu Tiwari
    • 1
  1. 1.ABV-Indian Institute of Information Technology and ManagementGwaliorIndia

Personalised recommendations