Skip to main content
SpringerLink
Log in

Significance of image representation for face verification

  • Original paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

In this paper we discuss the significance of representation of images for face verification. We consider three different representations, namely, edge gradient, edge orientation and potential field derived from the edge gradient. These representations are examined in the context of face verification using a specific type of correlation filter, called the minimum average correlation energy (MACE) filter. The different representations are derived using one-dimensional (1-D) processing of image. The 1-D processing provides multiple partial evidences for a given face image, one evidence for each direction of the 1-D processing. Separate MACE filters are used for deriving each partial evidence. We propose a method to combine the partial evidences obtained for each representation using an auto-associative neural network (AANN) model, to arrive at a decision for face verification. Results show that the performance of the system using potential field representation is better than that using the edge gradient representation or the edge orientation representation. Also, the potential field representation derived from the edge gradient is observed to be less sensitive to variation in illumination compared to the gray level representation of images.

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

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Li S.Z., Jain A.K. (2004). Handbook of Face Recognition. Springer, New York

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Baron R. (1981). Mechanism of human facial recognition. IEEE Trans. Syst. Man, Cybern. 15: 137–178

    Google Scholar 

  6. Kumar, B.V.K.V., Savvides, M., Venkataramani, K., Xie, C.: Spatial frequency domain image processing for biometric recognition. In: IEEE Proceedings of International Conference on Image Processing (ICIP), pp. 53–56. Rochester, New York (2002)

  7. Gao Y., Leung M.K.H. (2002). Face recognition using line edge map. IEEE Trans. Pattern Anal. Machine Intell. 24: 765–779

    Google Scholar 

  8. Manjunath, B.S., Chellappa, R., Malsburg, C.V.D.: A features based approach to face recognition. In: Proceedings of IEEE Internatinoal Conference Computer Vision Pattern recognition. pp. 373–378 (1992)

  9. Kotropoulos C.L., Tefas A., Pitas I. (2001). Using support vector machines to enhance the performance of elastic graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 23: 735–746

    Article  Google Scholar 

  10. Bruce, V., Hancock, P.J.B., Burton, A.M.: Human face recognition and identification. In: Proc. Face Recognition from Theory to Applications, pp. 373–378 (1998)

  11. Mahalanobis A., Kumar B.V.K.V., Casasent D. (1987). Minimum average correlation energy filters. Appl. Opt. 26: 3633–3640

    Google Scholar 

  12. Savvides, M.: Kumar, B.V.K.V.: Efficient design of advanced correlation filters for robust distortions-tolerant face identification. In: IEEE International Conference Advanced Video and Signal based Surveillance (2003)

  13. Kumar, P.K., Das, S., Yegnanarayana, B.: One-dimensional processing of images. In: Proceedings International Conference Multimedia Processing and Systems, IIT Madras. (2000)

  14. http://amp.ece.cmu.edu: (Advanced Multimedia Processing Lab web page at Electrical and Computer Engineering Department at CMU)

  15. Hester C.F., Casasent D. (1980). Multivariate technique for multiclass pattern recognition. Appl. Opt. 19: 1758–1761

    Google Scholar 

  16. Kumar B.V.K.V. (1992). Tutorial survey of composite filter designs for optical correltors. Appl. Opt. 31: 773–4801

    Google Scholar 

  17. Kumar B.V.K.V. (1986). Minimum variance synthetic discrimination function. J. Opt. Soc. Am. 3: 1579–1584

    Google Scholar 

  18. Yilmaz A., Gokmen M. (2000). Eigenhill vs. eigenface and eigenedge. Pattern Recogn. 34: 181–184

    Article  Google Scholar 

  19. Canny J. (1986). A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8: 679–698

    Article  Google Scholar 

  20. Oppenheim A.V., Schafer R.W. (1997). Discrete-time Signal Processing. Prentice Hall of India, New Delhi

    Google Scholar 

  21. Redner R.A., Walker H.F. (1984). Mixture densities, maximum likelihood and the EM algorithm. SIAM-REVIEW 26: 195–239

    Article  MATH  MathSciNet  Google Scholar 

  22. Yegnanarayana B., Kishore S.P. (2002). AANN: an alternative to GMM for pattern recognition. Neural Netw. 15: 459–469

    Article  Google Scholar 

  23. Xu, C., Prince, J.L.: Gradient vector flow: a new external force for snakes. In: IEEE Proc. Computer Vision Pattern Recognition, pp. 66–71 (1997)

  24. Horn B.K.P., Schunck B.G. (1991). Determining optical flow. Artif. Intell. 17: 185–203

    Article  Google Scholar 

  25. Courant R., Hilbert D. (1953). Methods of Mathematical Physics, Vol. 1. Interscience, New York

    Google Scholar 

  26. Charles A.H., Porsching T.A. (1990). Numerical Analysis of Partial Differential Equations. Prentice-Hall, Engelwood Cliffs

    MATH  Google Scholar 

  27. Sim, T., Baker, S., Bsat, M.: The CMU pose illumination, and expression PIE database of human faces. Tech. Report CMU-RI-TR-01-02, Robotics Institute, Carnegie Mellon University (2001)

  28. Savvides, M., Kumar, B.V.K.V.: Illumination normalization using logarithm transforms for face authentication. In: Audio and Video-Based Biometric Person Authentication (2003)

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Technology Madras, Chennai, 600036, Tamil Nadu, India

    Anil Kumar Sao

  2. International Institute of Information Technology, Hyderabad, 500032, Andhra Pradesh, India

    B. Yegnanarayana

  3. Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    B. V. K. Vijaya Kumar

Authors
  1. Anil Kumar Sao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. Yegnanarayana
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. B. V. K. Vijaya Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anil Kumar Sao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sao, A.K., Yegnanarayana, B. & Vijaya Kumar, B.V.K. Significance of image representation for face verification. SIViP 1, 225–237 (2007). https://doi.org/10.1007/s11760-007-0016-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-007-0016-5

Keywords

Search

Navigation