Making Hand Geometry Verification System More Accurate Using Time Series Representation with R-K Band Learning

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At present, applications of biometrics are rapidly increasing due to inconveniences in using traditional passwords and physical keys. Hand geometry, one of the most well-known biometrics, is implemented in many verification systems with various feature extraction methods. In recent work, a hand geometry verification system using time series conversion techniques and Dynamic Time Warping (DTW) distance measure with Sakoe-Chiba band has been proposed. This system demonstrates many advantages, especially ease of implementation and small storage space requirement using time series representation. In this paper, we propose a novel hand geometry verification system that exploits DTW distance measure and R-K band learning to further improve the system performance. Finally, our evaluation reveals that our proposed system outperforms the current system by a wide margin, in terms of False Acceptance Rate (FAR), False Rejection Rate (FRR), and Total Success Rate (TSR) at Equal Error Rate (EER).
In Proceedings of 11th National Computer Science and Engineering Conference (NCSEC 2007), Bangkok, Thailand, 19/11/2007. 8 pages, 10 figures

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Cryptography and Security

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http://salesiana.dossiersoluciones.com/handle/123456789/229910

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0905.1385
 http://arxiv.org/abs/0905.1385

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arXiv