Hand geometry identification without feature extraction by general regression neural network

Abstract

This paper presents an approach to automatically recognize hand geometry pattern based on a database. The system does not require any feature extraction stage before the identification. General regression neural networks are used for the classification and/or verification of the patterns. Simulation results show that hand geometry pattern identification by the proposed method improves the identification rate considerably. To show the system performance, false acceptance ratio and false rejection ratio are given. (c) 2006 Elsevier Ltd. All rights reserved.