Multibiometric: Feature Level Fusion Using FKP Multi-Instance biometric

TL;DR

This paper explores multi-instance feature level fusion for Finger Knuckle Print verification, demonstrating improved accuracy over single-instance methods using log-Gabor filter features and various fusion rules.

Contribution

It introduces a multi-instance fusion approach at the feature level for FKP verification, enhancing biometric performance compared to single-instance methods.

Findings

Multi-instance fusion outperforms single-instance verification.

Feature level fusion improves biometric accuracy.

Different fusion rules impact performance.

Abstract

This paper proposed the use of multi-instance feature level fusion as a means to improve the performance of Finger Knuckle Print (FKP) verification. A log-Gabor filter has been used to extract the image local orientation information, and represent the FKP features. Experiments are performed using the FKP database, which consists of 7,920 images. Results indicate that the multi-instance verification approach outperforms higher performance than using any single instance. The influence on biometric performance using feature level fusion under different fusion rules have been demonstrated in this paper.