Combined Haar-Hilbert and Log-Gabor Based Iris Encoders

TL;DR

This paper demonstrates that combining Haar-Hilbert and Log-Gabor iris encoders enhances recognition accuracy by reducing score distribution overlap, especially in dual iris approaches, leading to more reliable biometric decisions.

Contribution

It introduces a combined Haar-Hilbert and Log-Gabor iris encoder that improves recognition performance over individual encoders in dual iris systems.

Findings

Best performance achieved with combined encoder in dual iris approach

Score distribution overlap diminishes with combined encoder

Fusion of Haar-Hilbert and Log-Gabor channels enhances accuracy

Abstract

This chapter shows that combining Haar-Hilbert and Log-Gabor improves iris recognition performance leading to a less ambiguous biometric decision landscape in which the overlap between the experimental intra- and interclass score distributions diminishes or even vanishes. Haar-Hilbert, Log-Gabor and combined Haar-Hilbert and Log-Gabor encoders are tested here both for single and dual iris approach. The experimental results confirm that the best performance is obtained for the dual iris approach when the iris code is generated using the combined Haar-Hilbert and Log-Gabor encoder, and when the matching score fuses the information from both Haar-Hilbert and Log-Gabor channels of the combined encoder.