Hand Geometry Based Recognition with a MLP Classifier

TL;DR

This paper introduces a hand geometry biometric recognition system utilizing an MLP classifier, demonstrating high accuracy and low error rates on a custom database of hand images.

Contribution

It presents a new hand geometry database and evaluates an MLP-based recognition approach, achieving near-perfect identification and verification performance.

Findings

Up to 100% identification accuracy

Zero Detection Cost Function (DCF) in verification

Effective feature extraction for hand geometry

Abstract

This paper presents a biometric recognition system based on hand geometry. We describe a database specially collected for research purposes, which consists of 50 people and 10 different acquisitions of the right hand. This database can be freely downloaded. In addition, we describe a feature extraction procedure and we obtain experimental results using different classification strategies based on Multi Layer Perceptrons (MLP). We have evaluated identification rates and Detection Cost Function (DCF) values for verification applications. Experimental results reveal up to 100% identification and 0% DCF