Robust Features for Frontal Face Authentication in Difficult Image Conditions

  • Conrad Sanderson
  • Samy Bengio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2688)

Abstract

In this paper we extend the recently proposed DCT-mod2 feature extraction technique (which utilizes polynomial coefficients derived from 2D DCT coefficients obtained from horizontally & vertically neighbouring blocks) via the use of various windows and diagonally neighbouring blocks. We also propose enhanced PCA, where traditional PCA feature extraction is combined with DCT-mod2. Results using test images corrupted by a linear and a non-linear illumination change, white Gaussian noise and compression artefacts, show that use of diagonally neighbouring blocks and windowing is detrimental to robustness against illumination changes while being useful for increasing robustness against white noise and compression artefacts. We also show that the enhancedPCA technique retains all the positive aspects of traditional PCA (that is, robustness against white noise and compression artefacts) while also being robust to illumination changes; moreover, enhanced PCA outperforms PCA with histogram equalisation pre-processing.

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References

  1. [1]
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D. J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. Pattern Analysis and Machine Intelligence 19 (1997) 711–720.CrossRefGoogle Scholar
  2. [2]
    Chen, L-F., Liao, H-Y., Lin, J-C., Han, C-C.: Why recognition in a statistics-based face recognition system should be based on the pure face portion: a probabilistic decision-based proof. Pattern Recognition 34 (2001) 1393–1403.MATHCrossRefGoogle Scholar
  3. [3]
    Dempster, A.P., Laird, N.M., Rubin, D. B.: Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statistical Soc., Ser. B 39 (1977) 1–38.MATHMathSciNetGoogle Scholar
  4. [4]
    Doddington, G.R., Przybycki, M.A., Martin, A. F., Reynolds, D.A.: The NISTspeaker recognition evaluation-Overview, methodology, systems, results, perspective. Speech Communication 31 (2000) 225–254.CrossRefGoogle Scholar
  5. [5]
    Duc, B., Fischer, S., Bigün, J.: Face Authentication with Gabor Information on Deformable Graphs. IEEE Trans. Image Processing 8 (1999) 504–516.CrossRefGoogle Scholar
  6. [6]
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, USA, 2001.MATHGoogle Scholar
  7. [7]
    Eickeler, S., Müller, S., Rigoll, G.: Recognition of JPEG Compressed Face Images Based on Statistical Methods. Image and Vision Computing 18 (2000) 279–287.CrossRefGoogle Scholar
  8. [8]
    Gonzales, R. C., Woods, R. E.: Digital Image Processing. Addison-Wesley, 1993.Google Scholar
  9. [9]
    Koh, L.H., Ranganath, S., Venkatesh, Y.V.: An integrated automatic face detection and recognition system. Pattern Recognition 35 (2002) 1259–1273.MATHCrossRefGoogle Scholar
  10. [10]
    Lockie, M. (editor): Facial verification bureau launched by police IT group. Biometric Technology Today 10 (No. 3) (2002) 3–4.Google Scholar
  11. [11]
    Moon, H., Phillips, P. J.: Computational and performance aspects of PCA-based face-recognition algorithms. Perception 30 (2001) 303–321.CrossRefGoogle Scholar
  12. [12]
    Reynolds, D., Quatieri, T., Dunn, R.: Speaker Verification Using Adapted Gaussian Mixture Models. Digital Signal Processing 10 (2000) 19–41.CrossRefGoogle Scholar
  13. [13]
    Samaria, F.: Face Recognition Using Hidden Markov Models. PhD Thesis, University of Cambridge, 1994.Google Scholar
  14. [14]
    Sanderson, C., Paliwal, K.K.: Polynomial Features for Robust Face Authentication. Proc. Intern. Conf. on Image Processing, Rochester, New York, 2002, pp. 997–1000 (Vol. 3).Google Scholar
  15. [15]
    Sanderson, C.: The VidTIMIT Database. IDIAP Communication 02-06, Martigny, Switzerland, 2002.Google Scholar
  16. [16]
    Soong, F.K., Rosenberg, A.E.: On the Use of Instantaneous and Transitional Spectral Information in Speaker Recognition. IEEE Trans. Acoustics, Speech and Signal Processing 36 (1988) 871–879.MATHCrossRefGoogle Scholar
  17. [17]
    Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3 (1991) 71–86.CrossRefGoogle Scholar
  18. [18]
    Wallace, G.K.: The JPEG still picture compression standard. IEEE Trans. Consumer Electronics 38 (1992) xviii–xxxiv.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Conrad Sanderson
    • 1
  • Samy Bengio
    • 1
  1. 1.IDIAPMartignySwitzerland

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