Robust palmprint recognition based on the fast variation Vese-Osher model
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Palmprint is usually captured with a touchless device. Due to the changes of angle and position of the palm in a capturing process, as well as the defocus of device, it is inevitable to have some distortions in translation, rotation, and image blurriness, which would degrade the performance of a palmprint recognition system. In order to effectively solve the low recognition problem due to image blurriness, we propose a robust palmprint recognition system in this paper by using the fast Vese-Osher decomposition model to process the blurred palmprint images. First, a Gaussian defocus degradation model (GDDM) is proposed to characterize the image blurriness, and we can observe from this model that there are some stable features in the palmprint images with different scale of blurriness. Second, the structure layer and texture layer of blurred palmprint images are obtained by using the fast Vese-Osher decomposition model, and the structure layer is proved to be more stable and robust than the texture layer for palmprint recognition. Consequently, a novel algorithm based on the weighted histogram of oriented gradient for locally selected pattern (WHOG-LSP) is proposed and it is used to extract some robust features from the structure layer of blurred palmprint images. These extracted features can be used to address low performance issues associated with translation and rotation in palmprint recognition. Finally, the normalized correlation coefficient (NCC) is used to measure the similarity of palmprint features for the proposed recognition system. Extensive experiments on the PolyU palmprint database and the blurred PolyU palmprint database validate the effectiveness and real-time applicability of the proposed recognition system. In addition, an experiment is carried out in the IIT Delhi Touchless Palmprint Database for verifying the robustness of the proposed method.
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