Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/228
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Yang, J | - |
dc.creator | Zhang, DD | - |
dc.creator | Yang, JY | - |
dc.date.accessioned | 2014-12-11T08:22:50Z | - |
dc.date.available | 2014-12-11T08:22:50Z | - |
dc.identifier.issn | 1083-4419 | - |
dc.identifier.uri | http://hdl.handle.net/10397/228 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | en_US |
dc.rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | en_US |
dc.subject | Face recognition | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | Image representation | en_US |
dc.subject | Independent component analysis (ICA) | en_US |
dc.subject | Principal component analysis (PCA) | en_US |
dc.title | Constructing PCA baseline algorithms to reevaluate ICA-based face-recognition performance | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1015 | - |
dc.identifier.epage | 1021 | - |
dc.identifier.volume | 37 | - |
dc.identifier.issue | 4 | - |
dc.identifier.doi | 10.1109/TSMCB.2007.891541 | - |
dcterms.abstract | The literature on independent component analysis (ICA)-based face recognition generally evaluates its performance using standard principal component analysis (PCA) within two architectures, ICA Architecture I and ICA Architecture II. In this correspondence, we analyze these two ICA architectures and find that ICA Architecture I involves a vertically centered PCA process (PCA I), while ICA Architecture II involves a whitened horizontally centered PCA process (PCA II). Thus, it makes sense to use these two PCA versions as baselines to reevaluate the performance of ICA-based face-recognition systems. Experiments on the FERET, AR, and AT&T face-image databases showed no significant differences between ICA Architecture I (II) and PCA I (II), although ICA Architecture I (or II) may, in some cases, significantly outperform standard PCA. It can be concluded that the performance of ICA strongly depends on the PCA process that it involves. Pure ICA projection has only a trivial effect on performance in face recognition. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, Aug. 2007, v. 37, no. 4, p.1015-1021 | - |
dcterms.isPartOf | IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics | - |
dcterms.issued | 2007-08 | - |
dc.identifier.isi | WOS:000247833000021 | - |
dc.identifier.scopus | 2-s2.0-34547115694 | - |
dc.identifier.pmid | 17702297 | - |
dc.identifier.rosgroupid | r32790 | - |
dc.description.ros | 2006-2007 > Academic research: refereed > Publication in refereed journal | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | VoR allowed | en_US |
Appears in Collections: | Journal/Magazine Article |
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SMCB_C_37_4_07.pdf | 271.64 kB | Adobe PDF | View/Open |
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