Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/2621

Locally linear models on face appearance manifolds with application to dual-subspace based classification

Authors Fan, W.
Yeung, D.-Y.
Issue Date 2006
Source Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition , v. 2, 2006, p. 1384-1390
Summary Recently, there has been a flurry of research on face recognition based on multiple images or shots from either a video sequence or an image set. This paper is also such an attempt in multiple-shot face recognition. Specifically, we propose a novel nonparametric method that first extracts discriminating local models via clustering. We apply a hierarchical distance-based clustering procedure according to some distance measure on the appearance manifold to cluster similar face images together. Based on the local models extracted, we then construct the intrapersonal and extrapersonal subspaces. Given a new test image, the angle between the projections of the image onto the two subspaces is used as a distance measure for classification. Since a test example contains multiple face images in multiple-shot face recognition, the final classification combines the classification decisions of all individual test images via a majority voting scheme. We compare our method empirically with some previous methods based on a database of video sequences of human faces, showing that out method significantly outperforms other methods. © 2006 IEEE.
Subjects
ISSN 1063-6919
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Language English
Format Conference paper
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