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dc.contributor.authorShi, Q.en
dc.contributor.authorEriksson, A.en
dc.contributor.authorVan Den Hengel, A.en
dc.contributor.authorShen, C.en
dc.identifier.citationProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, held in Providence, Rhode Island USA, 20-25 June, 2011: pp. 553-560en
dc.description.abstractCompressive Sensing has become one of the standard methods of face recognition within the literature. We show, however, that the sparsity assumption which underpins much of this work is not supported by the data. This lack of sparsity in the data means that compressive sensing approach cannot be guaranteed to recover the exact signal, and therefore that sparse approximations may not deliver the robustness or performance desired. In this vein we show that a simple ℓ2 approach to the face recognition problem is not only significantly more accurate than the state-of-the-art approach, it is also more robust, and much faster. These results are demonstrated on the publicly available YaleB and AR face datasets but have implications for the application of Compressive Sensing more broadly.en
dc.description.statementofresponsibilityQinfeng Shi, Anders Eriksson, Anton van den Hengel and Chunhua Shenen
dc.relation.ispartofseriesIEEE Conference on Computer Vision and Pattern Recognitionen
dc.rightsCopyright 2011 IEEEen
dc.titleIs face recognition really a compressive sensing problem?en
dc.typeConference paperen
dc.contributor.conferenceConference on Computer Vision and Pattern Recognition (2011 : Rhode Island, USA)en
pubs.library.collectionComputer Science publicationsen
dc.identifier.orcidShi, Q. [0000-0002-9126-2107]en
dc.identifier.orcidVan Den Hengel, A. [0000-0003-3027-8364]en
dc.identifier.orcidShen, C. [0000-0002-8648-8718]en
Appears in Collections:Computer Science publications

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