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Recognizing body poses using multilinear analysis and semi-supervised learning
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DOI：10.1016/j.patrec.2009.06.007.png)
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Abstract
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This paper presents an approach to full-body human pose recognition using features extracted from stereo silhouettes via multilinear analysis in a semi-supervised learning framework. Inputs to the proposed approach are pairs of silhouette images obtained from wide baseline binocular cameras. Through multilinear analysis, low dimensional view-invariant pose coefficient vectors can be extracted from these stereo silhouette pairs. Taking these pose coefficient vectors as features, a recently proposed state-of-the-art semi-supervised learning method, Universum, is adopted for pose recognition. Experiment results obtained using real image data showed the efficacy of the proposed approach. (C) 2009 Elsevier B.V. All rights reserved.
Keywords:
Gesture recognition
Human-computer interaction
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