Jen-Mei Chang

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Illumination spaces capture how the appearances of human faces vary under changing illumination. This work models illumination spaces as points on a Grass-mann manifold and uses distance measures on this mani-fold to show that every person in the CMU-PIE and Yale data sets has a unique and identifying illumination space. This suggests that variations under(More)
Recent work has established that digital images of a human face, collected under various illumination conditions, contain discriminatory information that can be used in classification. In this paper we demonstrate that sufficient discriminatory information persists at ultra-low resolution to enable a computer to recognize specific human faces in settings(More)
The theory of illumination subspaces is well developed and has been tested extensively on the Yale Face Database B (YDB) and CMU-PIE (PIE) data sets. This paper shows that if face recognition under varying illumination is cast as a problem of matching sets of images to sets of images, then the minimal principal angle between subspaces is sufficient to(More)
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