Abstract
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.
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© 2004 Springer-Verlag Berlin Heidelberg
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You, J., Kong, WK., Zhang, D., Cheung, K.H. (2004). A New Approach to Personal Identification in Large Databases by Hierarchical Palmprint Coding with Multi-features. In: Zhang, D., Jain, A.K. (eds) Biometric Authentication. ICBA 2004. Lecture Notes in Computer Science, vol 3072. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25948-0_100
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DOI: https://doi.org/10.1007/978-3-540-25948-0_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22146-3
Online ISBN: 978-3-540-25948-0
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