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Type: Conference paper
Title: Is face recognition really a compressive sensing problem?
Author: Shi, Q.
Eriksson, A.
Van Den Hengel, A.
Shen, C.
Citation: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, held in Providence, Rhode Island USA, 20-25 June, 2011: pp. 553-560
Publisher: IEEE
Publisher Place: USA
Issue Date: 2011
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781457703942
ISSN: 1063-6919
Conference Name: Conference on Computer Vision and Pattern Recognition (2011 : Rhode Island, USA)
Statement of
Qinfeng Shi, Anders Eriksson, Anton van den Hengel and Chunhua Shen
Abstract: Compressive 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.
Rights: Copyright 2011 IEEE
RMID: 0020107126
DOI: 10.1109/CVPR.2011.5995556
Appears in Collections:Computer Science publications

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