Biometric identification by means of hand geometry and a neural net classifier

TL;DR

This paper presents a hand geometry biometric identification system utilizing neural network classifiers, analyzing feature discrimination and identification accuracy with a database of 22 individuals.

Contribution

It introduces a neural network-based approach for hand geometry biometrics and evaluates feature discrimination and identification performance.

Findings

Neural network classifiers achieve high identification accuracy.

Feature discrimination varies with different extracted features.

The system effectively identifies individuals based on hand geometry.

Abstract

This Paper describes a hand geometry biometric identification system. We have acquired a database of 22 people using a conventional document scanner. The experimental section consists of a study about the discrimination capability of different extracted features, and the identification rate using different classifiers based on neural networks.