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|Web of Science®
|A new distance criterion for face recognition using image sets
|Proceedings of the 7th Asian Conference on Computer Vision, Hyderabad, India, 2006: pp.549-558
|Lecture Notes in Computer Science; Volume 3851
|Asian Conference on Computer Vision (7th : 2006 : Hyderabad, India)
|Tat-Jun Chin and David Suter
|A major face recognition paradigm involves recognizing a person from a set of images instead of from a single image. Often, the image sets are acquired from a video stream by a camera surveillance system, or a combination of images which can be non-contiguous and unordered. An effective algorithm that tackles this problem involves fitting low-dimensional linear subspaces across the image sets and using a linear subspace as an approximation for the particular face identity. Unavoidably, the individual frames in the image set will be corrupted by noise and there is a degree of uncertainty on how accurate the resultant subspace approximates the set. Furthermore, when we compare two linear subspaces, how much of the distance between them is due to inter-personal differences and how much is due to intra-personal variations contributed by noise? Here, we propose a new distance criterion, developed based on a matrix perturbation theorem, for comparing two image sets that takes into account the uncertainty of estimating a linear subspace from noise affected image sets.
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