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

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).