This paper presents a new approach to personal identification using palmprints. To tackle the key issues such as feature extraction, representation, indexing, similarity measurement and fast search for the best match, we propose a hierarchical multi-feature coding scheme to facilitate coarse-to-fine matching for efficient and effective palmprint verification and identification in a large database. In contrast to the existing systems that employ a fixed mechanism for feature extraction and similarity measurement, we extract multiple features and adopt different matching criteria at different levels to achieve high performance by coarse-to-fine guided search. Our experimental results demonstrate the feasibility and effectiveness of the proposed method. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
You, J., Kong, W. K., Zhang, D., & Cheung, K. H. (2004). A new approach to personal identification in large databases by hierarchical palmprint coding with multi-features. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3072, 739–745. https://doi.org/10.1007/978-3-540-25948-0_100
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