Enhancing Recognition in Multimodal Biometric Systems: Score Normalization and Fusion of Online Signatures and Fingerprints

2024-03-26

Toufik Hafs *

Hatem Zehir

Ali Hafs

Brahmia, Hanene

Nait-Ali, Amine

DOI：10.59277/ROMJIST.2024.1.03

Original

Original request for help

Save

Abstract

En 中文

Multimodal biometrics employs multiple modalities within a single system to address the limitations of unimodal systems, such as incomplete data acquisition or deliberate fraud, while enhancing recognition accuracy. This study explores score normalization and its impact on system performance. To fuse scores effectively, prior normalization is necessary, followed by a weighted sum fusion technique that aligns impostor and genuine scores within a common range. Experiments conducted on three biometric databases demonstrate the promising efficacy of the proposed approach, particularly when combined with Empirical Modal Decomposition (EMD). The fusion system exhibits strong performance, with the best outcome achieved by merging the online signature and fingerprint modalities, resulting in a normalized Min-Max score-based Equal Error Rate (EER) of 1.69%.

Keywords:

Empirical mode decomposition

fingerprints

image and signal processing

min-max

multibiometrics

online handwritten signatures

scores fusion

weighted sum

Enhancing Recognition in Multimodal Biometric Systems: Score Normalization and Fusion of Online Signatures and Fingerprints

Abstract

Journal

Organization

Cited Papers

Citing Papers