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Type: Conference paper
Title: Incremental Kernel SVD for Face Recognition with Image Sets
Author: Chin, T.J.
Schindler, K.
Suter, D.
Citation: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition Southampton, U.K., 2006: pp.461-466
Publisher: IEEE
Publisher Place: Online
Issue Date: 2006
ISBN: 0769525032
Conference Name: International Conference on Automatic Face and Gesture Recognition (7th : 2006 : Southampton, U.K)
Statement of
Tat-Jun Chin, Konrad Schindler and David Suter
Abstract: Non-linear subspaces derived using kernel methods have been found to be superior compared to linear subspaces in modeling or classification tasks of several visual phenomena. Such kernel methods include Kernel PCA, Kernel DA, Kernel SVD and Kernel QR. Since incremental computation algorithms for these methods do not exist yet, the practicality of these methods on large datasets or online video processing is minimal. We propose an approximate incremental Kernel SVD algorithm for computer vision applications that require estimation of non-linear subspaces, specifically face recognition by matching image sets obtained through long-term observations or video recordings. We extend a well-known linear subspace updating algorithm to the nonlinear case by utilizing the kernel trick, and apply a reduced set construction method to produce sparse expressions for the derived subspace basis so as to maintain constant processing speed and memory usage. Experimental results demonstrate the effectiveness of the proposed method.
RMID: 0020093453
DOI: 10.1109/FGR.2006.67
Appears in Collections:Computer Science publications

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