Please use this identifier to cite or link to this item:
Scopus Web of ScienceĀ® Altmetric
Full metadata record
DC FieldValueLanguage
dc.contributor.authorChin, T.-
dc.contributor.authorSchindler, K.-
dc.contributor.authorSuter, D.-
dc.identifier.citationProceedings of the 7th International Conference on Automatic Face and Gesture Recognition Southampton, U.K., 2006: pp.461-466-
dc.description.abstractNon-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.-
dc.description.statementofresponsibilityTat-Jun Chin, Konrad Schindler and David Suter-
dc.titleIncremental Kernel SVD for Face Recognition with Image Sets-
dc.typeConference paper-
dc.contributor.conferenceInternational Conference on Automatic Face and Gesture Recognition (7th : 2006 : Southampton, U.K)-
dc.identifier.orcidSuter, D. [0000-0001-6306-3023]-
Appears in Collections:Aurora harvest
Computer Science publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.