Adaptive Contrast Enhancement and Optimised Feature Matching for RootSIFT-Based Palm-Vein Recognition

2026-07-17

AI Summary

This study addresses the degraded recognition performance of low-contrast palm vein images caused by near-infrared scattering and sensor limitations. The authors propose an enhanced method, ILACS-BGOT, which improves local contrast while effectively suppressing block artifacts. They further develop a recognition pipeline integrating RootSIFT features, KNN combined with Random Sample Consensus (RANSAC)-based transformation (RT) matching, and Mean and Median Distance (MMD) filtering. Notably, the original ILACS-LGOT is innovatively refined into ILACS-BGOT to better preserve fine details. The work also provides a systematic analysis of how MMD and RT parameters influence cross-dataset generalization. Evaluated on the CASIA, PolyU, and PUT datasets, the proposed approach significantly outperforms existing methods, achieving substantially lower equal error rates (EER) and higher accuracy, with performance consistently improving as template size increases.

Abstract

Palm-vein recognition is a highly secure biometric modality due to the uniqueness and subcutaneous nature of vein patterns. However, low contrast in palm-vein images, caused by NIR light scattering and sensor limitations, remains a significant challenge. To address this, we propose the Intensity-Limited Adaptive Contrast Stretching with Bidirectional Gaussian-weighted Overlapping Tiles (ILACS-BGOT) method, an enhancement of the previously developed ILACS with Layered Gaussian-weighted Overlapping Tiles (ILACS-LGOT) technique. ILACS enhances local contrast, while BGOT mitigates blocky artefacts. This study further integrates RootSIFT features with KNN+RT and incorporates the previously introduced Mean and Median Distance (MMD) filter to investigate the parameter variations of both MMD and RT, and their impact on recognition performance. A comprehensive analysis was conducted across three benchmark datasets (CASIA, PolyU, and PUT), using 42 combinations of MMD filter thresholds and RT values. Results were evaluated using EER and Accuracy. Findings reveal that higher template sizes improve performance, while varying MMD thresholds reflect dataset-specific rotational variations. The proposed system demonstrates superior generalisability, achieving significant improvements in both EER and Accuracy over existing methods. Furthermore, the underlying ILACS-BGOT mechanism suggests potential applicability beyond palm vein recognition to other biometric modalities such as finger vein and palmprint recognition, and more generally to low-contrast image enhancement across computer vision applications.

Problem

• palm-vein recognition
• low contrast
• NIR imaging
• biometric modality
• image enhancement

Innovation

• ILACS-BGOT
• RootSIFT
• MMD filter
• adaptive contrast enhancement
• feature matching