Abstract
In this paper we illustrate the implementation of a robust, real-time biometric system for identity verification based on palmprint images. The palmprint images are preprocessed to align the major axes of hand shapes and to extract the palm region. We extract features using Scale Invariant Feature Transform (SIFT). Classification of individual SIFT features is done through KNN. The class of the hand image is decided by a majority based voting among its classified SIFT features. We demonstrate on the CASIA and PolyU datasets that the proposed system achieves authentication accuracy comparable to other state of the art algorithms.
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Acknowledgments
This research is part of project "Expression Recognition based on Facial Anatomy", grant number 109E061, supported by The Support Programme for Scientific and Technological Research Projects (1001) of The Scientific and Technological Research Council of Turkey (T \(\ddot{\text U} \text {B} \dot{\text I}\)TAK). Portions of the research in this paper use CASIA-Iris V3 database collected by the Chinese Academy of Sciences Institute of Automation (CASIA) and the PolyU database collected by the Department of Computing in Hong Kong Polytechnic University.
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Abeysundera, H.P., Eskil, M.T. (2011). Palmprint Verification Using SIFT Majority Voting. In: Gelenbe, E., Lent, R., Sakellari, G. (eds) Computer and Information Sciences II. Springer, London. https://doi.org/10.1007/978-1-4471-2155-8_37
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DOI: https://doi.org/10.1007/978-1-4471-2155-8_37
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