A Framework for Face Recognition Based on Fuzzy Minimal Structure Oscillation and Independent Component Analysis

  • Sharmistha Bhattacharya (Halder)Email author
  • Srijita Barman Roy
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 11)


This paper aims to provide a better accuracy for face recognition procedure. This new algorithm is based on accurate feature extraction and proper classification. In this paper feature coordinate-based ICA is used for feature extraction. Pixel values of invariable coordinates (containing decisive data) for every training set are considered for analyzing through ICA. After feature extraction, these values are used for fuzzy minimal structure oscillation-based classification. Proposed face recognition procedure accentuates improved classification considering the feature vectors, which is the outcome of independent component analysis of the face image.


Fuzzy minimal structure oscillation Feature coordinate Feature coordinate-based ICA Face recognition 


  1. 1.
    Popa, V., Noiri, T.: On M-continuous functions. AnalUnivDunarea Jos-Galati, Ser Mat Fiz MecTeorFasc II 18(23), 31–41 (2000)zbMATHGoogle Scholar
  2. 2.
    Maki, H.: Generalized-sets and the associated closure operator. Special issue on commemoration of Prof KazusadaIkedas Retirement, pp. 139–146 (1986)Google Scholar
  3. 3.
    Alimohammady, M., Roohi, M.: Fuzzy minimal structure and fuzzy minimal vector spaces. Chaos, Solitons Fractals 27(3), 599–605 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Mukherjee, A., Halder, S.: Some more results on oscillatory region of a fuzzy set. In: International Conference on Modeling and Simulation CIT, Coimbatore, pp. 665–670 (2007)Google Scholar
  5. 5.
    Bhattacharya (Halder), S., Roy, S.: On Fuzzy m x* oscillation and it’s application in image processing. Ann. Fuzzy Math. Inform. 7(2), 319–329 (2014)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Bhattacharya (Halder), S., Barman Roy, S., Saha, S.: Application of fuzzy oscillation in the field of face recognition. In: International Symposium on Advanced Computing and Communication, IEEE, Silchar, pp. 192–107 (2015)Google Scholar
  7. 7.
  8. 8.
  9. 9.
    Lihong, Z., Ye, W., Hongfeng, T.: Face recognition based on independent component analysis. In: Chinese Control and Decision Conference (CCDC), pp. 426–429. IEEE (2011). ISSN: 1948-9439Google Scholar
  10. 10.
    Falco, N., Benediktsson, J., Bruzzone, L.: Spectral and spatial classification of hyperspectral images based on ICA and reduced morphological attribute profiles. IEEE Trans. Geosci. Remote Sens. 53(11), 6223–6240 (2015). doi: 10.1109/TGRS.2015.2436335
  11. 11.
    Karimi, M.M., Zadeh, S.H.: Face recognition: a sparse representation-based classification using independent component analysis. In: 6th International Symposium on Telecommunications (IST’2012), pp. 1170–1174. IEEE. 978-1-4673-2073-3Google Scholar
  12. 12.
    Yi-qiong, X., Bi-cheng, L., Bo, W.: Face recognition by fast independent component analysis and genetic algorithm. In: Proceedings of the Fourth International Conference on Computer and Information Technology (CIT’04), pp. 194–198. IEEE (2004). doi: 10.1109/CIT.2004.1357196
  13. 13.
    Karande, J.K., Talbar, S.N.: Independent component analysis of edge information for face recognition. In: Springer Briefs In Applied Sciences And Technology, Computational Intelligence. Springer, New Delhi. ISBN 978-81-322-1512-7. doi: 10.1007/978-81-322-1512-7
  14. 14.
    Zhang, X., Zhang, X., Ren, X.: Two dimensional principal component analysis based independent component analysis for face recognition. In: International Conference on Multimedia Technology (ICMT), pp. 934–936. IEEE, Hangzhou (2011). doi: 10.1109/ICMT.2011.6002199
  15. 15.
    Marques, I., Gran˜a, M.: Face recognition with lattice independent component analysis and extreme learning machines. Soft Comput. 16, 1525–1537 (2012). doi: 10.1007/s00500-012-0826-4,1525-1537
  16. 16.
    Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10(3), 626–634 (1999)CrossRefGoogle Scholar
  17. 17.
    Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE, Proc. Trans. Neural Netw. 13, 1450–1464 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Sharmistha Bhattacharya (Halder)
    • 1
    Email author
  • Srijita Barman Roy
    • 1
  1. 1.Department of MathematicsTripura UniversityAgartalaIndia

Personalised recommendations