By Kresimir Delac, Mislav Grgic (Editors)
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The observed phenomenon is well understood in the context of energy of intrinsic and extrinsic image differences and noise (see  for a thorough discussion). Higher than average recognition rates for raw input correspond to small changes in imaging conditions between training and test, and hence lower energy of extrinsic variation. In this case, the two filters decrease the signal-to-noise ratio, worsening Achieving Illumination Invariance using Image Filters 19 the performance, see Figure 5 (a).
European Conference on Computer Vision (ECCV), 4:2740, May 2006.  O. Arandjelovic, G. Shakhnarovich, J. Fisher, R. Cipolla, and T. Darrell. Face recognition with image sets using manifold density divergence. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1:581-588, June 2005.  O. Arandjelovic and A. Zisserman. Automatic face recognition for film character retrieval in feature-length films. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1:860-867, June 2005.
2 Learning the Į - function of the joint To learn the a-function ǂ* (μ) as defined in (3), we first need an estimate probability density p(ǂ, μ) as per (6). The main difficulty of this problem is of practical nature: in order to obtain an accurate estimate using one of many off-the-shelf density estimation techniques, a prohibitively large training database would be needed to ensure a well sampled distribution of the variable μ. Instead, we propose a heuristic alternative which, we will show, will allow us to do this from a small training corpus of individuals imaged in various illumination conditions.