Skip to main content
SpringerLink
Log in

Binarized eigenphases applied to limited memory face recognition systems

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

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.

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Chellappa R, Wilson CL, Sirohey N (1995) Human and machine recognition of faces, a survey. Proc IEEE 83:705–740

    Article  Google Scholar 

  2. Brunelli R, Poggio T (1993) Face recognition: features versus templates. IEEE Trans Pattern Anal Mach Intell 15(10):1042–1052

    Article  Google Scholar 

  3. Rowley HA, Baluja S, Kanade T (1998) Neural network-based face detection. IEEE Trans Pattern Anal Mach Intell 20(1):23–28

    Article  Google Scholar 

  4. Moghaddam B, Pentlend A (1997) Probabilistic visual learning for object representation. IEEE Trans Pattern Anal Mach Intell 19(7):696–710

    Article  Google Scholar 

  5. Sung KK, Poggio T (1998) Example-based learning for view-based human face detection. IEEE Trans Pattern Anal Mach Intell 20(1):39–51

    Article  Google Scholar 

  6. Cai J, Goshtasby A (1999) Detecting human faces in color images. Image Vis Comput 63–75

  7. Lee K, Byun H (2003) A new face authentication system for memory-constrained devices. IEEE Trans Consumer Electron 49(4):1214–1222

    Article  Google Scholar 

  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, USA

  9. Zaeri N, Mokhtarian F, Cherri A (2006) Fast face recognition techniques for small and portable devices. Proceedings of the IEEE, Italy

  10. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3:71–86

    Article  Google Scholar 

  11. Zhang J, Yan Y, Lades M (1997) Face recognition: eigenface, elastic matching, and neural nets. Proc IEEE 85(9):1422

    Article  Google Scholar 

  12. Etemad K, Chellappa R (1997) Discriminant analysis for recognition of human face images. J Opt Soc Am A 14(8):1724–1733

    Article  Google Scholar 

  13. Kim HC, Kim D, Bang SY (2002) Face retrieval using 1st- and 2nd-order PCA mixture model. International conference on image processing, Rochester, NY

  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 2005

  15. Savvides M, Vijaya Kumar BVK, Khosla PK (2004) Eigenphases vs. eigenfaces. IEEE-17th international conference on pattern recognition

  16. Mokhtarian F, Bober M (2003) Curvature scale space: theory, applications, and MPEG-7 standardization. Kluwer, Netherlands

    MATH  Google Scholar 

  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/N6188

  18. Kamei T, Yamada A (2002) Report of core experiment on Fourier spectral PCA based face description, ISO/IEC JTC1/SC21/WG11 M8277, Fairfax, VA

  19. Oppenheim AV, Lim JS (1981) The importance of phase in signals. Proc IEEE 69:529–541

    Article  Google Scholar 

  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 authentication

  21. Phillips P, Wechsler H, Huang J, Rauss P (1998) The FERET database and evaluation procedure for face-recognition algorithms. J Image Vis Comput 295–306

  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 910–916

    Google Scholar 

  23. Moghaddam B, Pentland A (1997) Probabilistic visual learning for object representation. IEEE Trans Pattern Anal Mach Intell 19(7):696–710

    Article  Google Scholar 

  24. Moghaddam B (2002) Principal manifolds and probabilistic subspace for visual recognition. IEEE Trans Pattern Anal Mach Intell 24(6):780–788

    Article  Google Scholar 

Download references

Acknowledgments

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

Author information

Authors and Affiliations

  1. Electronic and Electrical Engineering Department, University of Surrey, GU2 7XH, Guildford, United Kingdom

    Naser Zaeri & Farzin Mokhtarian

  2. Electrical Engineering Department, Kuwait University, PO Box 5969, Safat, Kuwait

    Abdallah Cherri

Authors
  1. Naser Zaeri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farzin Mokhtarian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdallah Cherri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Naser Zaeri.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zaeri, N., Mokhtarian, F. & Cherri, A. Binarized eigenphases applied to limited memory face recognition systems. Pattern Anal Applic 11, 373–383 (2008). https://doi.org/10.1007/s10044-008-0129-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-008-0129-7

Keywords

Search

Navigation