Fusion Based Hand Geometry Recognition Using Dempster-Shafer Theory

TL;DR

This paper introduces a novel hand geometry recognition method that fuses features from both hands using Dempster-Shafer theory, achieving high accuracy without pose restrictions.

Contribution

It proposes a new fusion technique at feature and decision levels combined with probability algorithms for improved person recognition.

Findings

Correct identification rate of 99.5%

False Acceptance Rate of 0.625%

Effective fusion of hand features enhances recognition accuracy

Abstract

This paper presents a new technique for person recognition based on the fusion of hand geometric features of both the hands without any pose restrictions. All the features are extracted from normalized left and right hand images. Fusion is applied at feature level and also at decision level. Two probability based algorithms are proposed for classification. The first algorithm computes the maximum probability for nearest three neighbors. The second algorithm determines the maximum probability of the number of matched features with respect to a thresholding on distances. Based on these two highest probabilities initial decisions are made. The final decision is considered according to the highest probability as calculated by the Dempster-Shafer theory of evidence. Depending on the various combinations of the initial decisions, three schemes are experimented with 201 subjects for identification and verification. The correct identification rate found to be 99.5%, and the False Acceptance Rate (FAR) of 0.625% has been found during verification.