Pattern Analysis and Applications

, Volume 11, Issue 3–4, pp 373–383 | Cite as

Binarized eigenphases applied to limited memory face recognition systems

Theoretical Advances

Abstract

Most of the algorithms proposed for face recognition involve considerable amount of computations and hence they cannot be used on devices constrained with limited memory. In this paper, we propose a novel solution for efficient face recognition problem for the systems that utilize low memory devices. The new technique applies the principal component analysis to the binarized phase spectrum of the Fourier transform of the covariance matrix constructed from the MPEG-7 Fourier Feature Descriptor vectors of the images. The binarization step that is applied to the phases adds many interesting advantages to the system. It will be shown that the proposed technique increases the face recognition rate and at the same time achieves substantial savings in the computational time, when compared to other known systems. Experiments on two independent databases of face images are reported to demonstrate the effectiveness of the proposed technique.

Keywords

Face recognition Limited memory PCA MPEG-7 

Notes

Acknowledgments

The authors would like to thank the reviewers for the constructive suggestions and valuable comments.

References

  1. 1.
    Chellappa R, Wilson CL, Sirohey N (1995) Human and machine recognition of faces, a survey. Proc IEEE 83:705–740CrossRefGoogle Scholar
  2. 2.
    Brunelli R, Poggio T (1993) Face recognition: features versus templates. IEEE Trans Pattern Anal Mach Intell 15(10):1042–1052CrossRefGoogle Scholar
  3. 3.
    Rowley HA, Baluja S, Kanade T (1998) Neural network-based face detection. IEEE Trans Pattern Anal Mach Intell 20(1):23–28CrossRefGoogle Scholar
  4. 4.
    Moghaddam B, Pentlend A (1997) Probabilistic visual learning for object representation. IEEE Trans Pattern Anal Mach Intell 19(7):696–710CrossRefGoogle Scholar
  5. 5.
    Sung KK, Poggio T (1998) Example-based learning for view-based human face detection. IEEE Trans Pattern Anal Mach Intell 20(1):39–51CrossRefGoogle Scholar
  6. 6.
    Cai J, Goshtasby A (1999) Detecting human faces in color images. Image Vis Comput 63–75Google Scholar
  7. 7.
    Lee K, Byun H (2003) A new face authentication system for memory-constrained devices. IEEE Trans Consumer Electron 49(4):1214–1222CrossRefGoogle Scholar
  8. 8.
    Ng C, Savvides M, Khosla PK (2005) Real-time face verification system on a cell-phone using advanced correlation filters. Automatic identification advanced technologies, fourth IEEE workshop on 57–62, New York, USAGoogle Scholar
  9. 9.
    Zaeri N, Mokhtarian F, Cherri A (2006) Fast face recognition techniques for small and portable devices. Proceedings of the IEEE, ItalyGoogle Scholar
  10. 10.
    Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3:71–86CrossRefGoogle Scholar
  11. 11.
    Zhang J, Yan Y, Lades M (1997) Face recognition: eigenface, elastic matching, and neural nets. Proc IEEE 85(9):1422CrossRefGoogle Scholar
  12. 12.
    Etemad K, Chellappa R (1997) Discriminant analysis for recognition of human face images. J Opt Soc Am A 14(8):1724–1733CrossRefGoogle Scholar
  13. 13.
    Kim HC, Kim D, Bang SY (2002) Face retrieval using 1st- and 2nd-order PCA mixture model. International conference on image processing, Rochester, NYGoogle Scholar
  14. 14.
    Kong H, Li X, Wang J-G, Teoh EK, Kambhamettu C (2005) Discriminant low-dimensional subspace analysis for face recognition with small number of training samples. British machine vision conference (BMVC), Oxford, UK, 5–9 September 2005Google Scholar
  15. 15.
    Savvides M, Vijaya Kumar BVK, Khosla PK (2004) Eigenphases vs. eigenfaces. IEEE-17th international conference on pattern recognitionGoogle Scholar
  16. 16.
    Mokhtarian F, Bober M (2003) Curvature scale space: theory, applications, and MPEG-7 standardization. Kluwer, NetherlandsMATHGoogle Scholar
  17. 17.
    Lipinski L, Yamada A (2003) MPEG-7 Face recognition technique, international organization for standardization, ISO/IEC JTC1/SC29/WG11, coding of moving pictures and audio, NPEG03/N6188Google Scholar
  18. 18.
    Kamei T, Yamada A (2002) Report of core experiment on Fourier spectral PCA based face description, ISO/IEC JTC1/SC21/WG11 M8277, Fairfax, VAGoogle Scholar
  19. 19.
    Oppenheim AV, Lim JS (1981) The importance of phase in signals. Proc IEEE 69:529–541CrossRefGoogle Scholar
  20. 20.
    Messer K, Matas J, Kittler J, Luettin J, Maitre G (1999) XM2VTSDB: The extended M2VTS database. Second international conference on audio and video-based biometric person authenticationGoogle Scholar
  21. 21.
    Phillips P, Wechsler H, Huang J, Rauss P (1998) The FERET database and evaluation procedure for face-recognition algorithms. J Image Vis Comput 295–306Google Scholar
  22. 22.
    Moghaddam B, Jebara T, Pentland A (1999) Bayesian modeling of facial similarity. In: Kearns MJ, Solla SA, Cohn DA (eds) Advances in neural information processing system 11. MIT Press, Cambridge, pp 910–916Google Scholar
  23. 23.
    Moghaddam B, Pentland A (1997) Probabilistic visual learning for object representation. IEEE Trans Pattern Anal Mach Intell 19(7):696–710CrossRefGoogle Scholar
  24. 24.
    Moghaddam B (2002) Principal manifolds and probabilistic subspace for visual recognition. IEEE Trans Pattern Anal Mach Intell 24(6):780–788CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  • Naser Zaeri
    • 1
  • Farzin Mokhtarian
    • 1
  • Abdallah Cherri
    • 2
  1. 1.Electronic and Electrical Engineering DepartmentUniversity of SurreyGuildfordUnited Kingdom
  2. 2.Electrical Engineering DepartmentKuwait UniversitySafatKuwait

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