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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Robust Blurred Palmprint Recognition via the Fast Vese-Osher Model
verfasst von : Danfeng Hong, Wanquan Liu, Jian Su, Zhenkuan Pan, Xin Wu
Erschienen in: Computational Intelligence, Networked Systems and Their Applications
Verlag: Springer Berlin Heidelberg
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In this paper, we propose a new palmprint recognition system by using the fast Vese-Osher decomposition model to process the blurred palmprint images. First, a Gaussian defocus degradation model (GDDM) is proposed to extract the structure layer and texture layer of blurred palmprint images by using the fast Vese-Osher decomposition model, and the structure layer is proved to be more stable and robust than texture layer for palmprint recognition. Second, a novel algorithm based on weighted robustness with histogram of oriented gradient (WRHOG) is proposed to extract robust features from the structure layer of blurred palmprint images, which can address the problem of translation and rotation to a large extent. Finally, the normalized correlation coefficient (NCC) is used to measure the similarity of palmprint features for the new recognition system. Extensive experiments on the PolyU palmprint database and the blurred PolyU palmprint database validate the effectiveness of the proposed recognition system.