Hand Geometry Recognition System

Abstract

This paper presents a useful biological approach for hand geometry￾based recognition systems. Measurable hand geometry such as width, length, and finger area, were used to generate feature vectors. As useful properties, thirty￾five hand-shaped geometry scales are used. Artificial neural networks are used as distinct classifiers. The experimental result of all dataset reaches to the performance of 98.30% as recognition rate