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• W4285177866 abstract "Phase-based image matching has shown high recognition accuracy in palmprint verification. The algorithm compares a pair of palmprint images by extracting local phase features from the images and computing local correlation functions between them. A major drawback of this algorithm is its high computational cost associated with the evaluation of local correlation functions. This needs to be addressed, especially in the case of one-to-many comparisons required for palmprint identification. The problem becomes increasingly severe as the number of enrolled images increases. In this paper, we propose a novel palmprint identification algorithm with low computational complexity, which employs a sparse representation of enrolled phase features (i.e., phase templates) to evaluate local correlation functions. For this purpose, we also develop an efficient Convolutional Sparse Coding (CSC) algorithm that can derive a compact representation of phase templates. The proposed method reduces the computational cost of phase-based palmprint identification without significant degradation of recognition performance. Our experiments using public databases clearly demonstrate the advantage of the proposed method over conventional methods." @default.
• W4285177866 created "2022-07-14" @default.
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• W4285177866 date "2022-07-01" @default.
• W4285177866 modified "2023-10-14" @default.
• W4285177866 title "Phase-Based Palmprint Identification With Convolutional Sparse Coding" @default.
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• W4285177866 doi "https://doi.org/10.1109/tbiom.2022.3183568" @default.
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