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Illumination invariance remains the most researched, yet the most challenging aspect of automatic face recognition. In this paper, the authors investigate the discriminative power of colour-based invariants in the presence of large illumination changes between training and test data, when appearance changes due to cast shadows and non-Lambertian effects are significant. Specifically, there are three main contributions: they employ a more sophisticated photometric model of the camera and show how its parameters can be estimated, they derive several novel colour-based face invariants, and on a large database of video sequences they examine and evaluate the largest number of colour-based representations in the literature.
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