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)


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 enhanced PCA 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.


Principal Component Analysis Feature Extraction Discrete Cosine Transform Face Image Gaussian Mixture Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

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

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