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Competitive coding scheme for palmprint verification

Kong, A.W.-K., Zhang, D.·2004·Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.

Abstract

There is increasing interest in the development of reliable, rapid and non-intrusive security control systems. Among the many approaches, biometrics such as palmprints provide highly effective automatic mechanisms for use in personal identification. This paper presents a new method for extracting features from palmprints using the competitive coding scheme and angular matching. The competitive coding scheme uses multiple 2-D Gabor filters to extract orientation information from palm lines. This information is then stored in a feature vector called the competitive code. The angular matching with an effective implementation is then defined for comparing the proposed codes, which can make over 9,000 comparisons within 1s. In our testing database of 7,752 palmprint samples from 386 palms, we can achieve a high genuine acceptance rate of 98.4% and a low false acceptance rate of 3/spl times/10/sup -6/%. The execution time for the whole process of verification, including preprocessing, feature extraction and final matching, is 1s.

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Metadata

Authors
Kong, A. W.-K., & Zhang, D.
Venue
Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.
Publisher
IEEE
Published
2004
Pages
520-523 Vol.1
ISSN
1041-3278 (ISSN-L)
OpenAlex
W4243097765
Cited by
552
References
0
FWCI
6.78
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Competitive coding scheme for palmprint verification. https://app.cochat.ai/papers/10.1109/icpr.2004.1334184
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Kong, A. W.-K., & Zhang, D. (2004). Competitive coding scheme for palmprint verification. Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. https://doi.org/10.1109/icpr.2004.1334184

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.