Analysis and Performance Evaluation of ICA-Based Architectures for Face Recognition

  • Anu Singha
  • Mrinal Kanti BhowmikEmail author
  • Prasenjit Dhar
  • Anjan Kumar Ghosh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9448)


Prediction of the best ICA architecture for face recognition systems is somewhat complicated. This paper shows how the recognition performance of both architectures depends on the nature of feature vectors rather than several criteria such as different databases, number of subjects, and number of principle components. The investigation finds that Architecture-II yields the better performance than Architecture-I based on face feature vectors. The experiments are done on different face datasets like FERET, ORL, CVL, and YALE.


ICA Architecture-I Architecture-II Performance evaluation Analysis 



The work presented here is being conducted in the Biometrics Laboratory and Bio-Medical Infrared Image Processing Laboratory of Department of Computer Science and Engineering of Tripura University (A Central University), Tripura, Suryamaninagar-799022. The research work was supported by the Grant No. 12(2)/2011-ESD, Dated 29/03/2011 from the DeitY, MCIT, Government of India and also supported by the Grant No. BT/533/NE/-TBP/2013, Dated 03/03/2014 from the Department of Biotechnology (DBT), Government of India. The authors would like to thank Prof. Barin Kumar De, Department of Physics, Tripura University (A Central University) and Dr. Debotosh Bhattacharjee, Associate Professor, Department of Computer Science and Engineering, Jadavpur University for their kind support to carry out this research work.


  1. 1.
    Bartlett, M.S., Lades, H.M., Sejnowski, T.J.: Independent component representations for face recognition. In: Proceedings of the SPIE Symposium on Electronic Imaging: Science and Technology; Conference on Human Vision and Electronic Imaging III, San Jose, California (1998)Google Scholar
  2. 2.
    Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Trans. Neural Netw. 13, 1450–1464 (2002)CrossRefGoogle Scholar
  3. 3.
    Bell, A., Sejnowski, T.: An information maximization approach to blind separation and blind deconvolution. J. Neural Comput. 37, 1129–1159 (2007)Google Scholar
  4. 4.
    Comon, P.: Independent component analysis: a new concept? Signal Process. 36, 287–314 (1994)zbMATHCrossRefGoogle Scholar
  5. 5.
    Deniz, O., Castrillon, M., Hernandez, M.: Face recognition using independent component analysis and support vector machines. Pattern Recogn. Lett. 24, 2153–2157 (2001)CrossRefGoogle Scholar
  6. 6.
    Draper, B.A., Baek, K., Bartlett, M.S., Beveridge, J. R.: Recognizing faces with PCA and ICA. Comput. Vis. Image Underst. 91, 115–137 (2003)Google Scholar
  7. 7.
    Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. PAMI 23, 643–660 (2001)CrossRefGoogle Scholar
  8. 8.
    Guo, X., Zhang, X., Deng, C., Wei, J.: Facial expression recognition based on independent component analysis. J. Multimedia 8, 402–409 (2013)Google Scholar
  9. 9.
    Hyvarinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Netw. 13, 411–430 (2000)CrossRefGoogle Scholar
  10. 10.
    Kinage, K.S., Bhirud, S.G.: Face recognition using independent component analysis of GaborJet (GaborJet-ICA). In: IEEE International Colloquium on Signal Processing and Its Applications (CSPA), Malacca City, pp. 1–6 (2010)Google Scholar
  11. 11.
    Liu, C., Wechsler, H.: Comparative assessment of independent component analysis (ICA) for face recognition. In: International Conference on Audio and Video Based Biometric Person Authentication, Washington (1999)Google Scholar
  12. 12.
    Liu, C.: Enhanced independent component analysis and its application to content based face image retrieval. IEEE Trans. Syst. Man Cybern. B Cybern. 34, 1117–1127 (2004)CrossRefGoogle Scholar
  13. 13.
    Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.J.: The FERET database and evaluation procedure for face-recognition algorithms. Image Vision Comput. 16, 295–306 (1998)CrossRefGoogle Scholar
  14. 14.
    Sirovich, L., Kirby, M.: Low-dimensional procedure for characterization of human faces. J. Opt. Soc. Am. A 4(3), 519–524 (1987)CrossRefGoogle Scholar
  15. 15.
    Socolinsky, D.A., Selinger, A.: A comparative analysis of face recognition performance with visible and thermal infrared imagery. In: Proceedings of the International Conference on Pattern Recognition, Quebec City (2002)Google Scholar
  16. 16.
    Solina, F., Peer, P., Batagelj, B., Juvan, S., Kovac, J.: Color-based face detection in the “15 seconds of fame” art installation. In: Mirage 2003, Conference on Computer Vision/Computer Graphics Collaboration for Model-based Imaging, Rendering, Image Analysis and Graphical Special Effects, pp. 38–47. INRIA Rocquencourt, France, Wilfried Philips, Rocquencourt, INRIA (2003)Google Scholar
  17. 17.
  18. 18.
    Yang, J., Zhang, D., Jing-Yu, Y.: Constructing PCA baseline algorithms to reevaluate ICA-based face-recognition performance. IEEE Trans. Syst. Man Cybern.-Part B Cybern. 37, 1015–1021 (2007)CrossRefGoogle Scholar
  19. 19.
    Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.: Face recognition: a literature survey. Technical report, University of Maryland, College Park, MD (2002). Technical report, Global Grid Forum (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Anu Singha
    • 1
  • Mrinal Kanti Bhowmik
    • 1
    Email author
  • Prasenjit Dhar
    • 1
  • Anjan Kumar Ghosh
    • 1
  1. 1.Department of Computer Science and EngineeringTripura University (A Central University)SuryamaninagarIndia

Personalised recommendations