基于RootSIFT的掌静脉识别的自适应对比度增强和优化特征匹配
Adaptive Contrast Enhancement and Optimised Feature Matching for RootSIFT-Based Palm-Vein Recognition
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中文总结 AI 辅助
研究针对掌静脉图像对比度低的问题，提出ILACS-BGOT方法增强对比度并减轻伪影，集成RootSIFT特征与KNN+RT及MMD滤波器，通过在三个数据集上分析参数变化对识别性能的影响，显著提升了掌静脉识别的EER和准确率，且有更广泛适用性。
中文摘要 AI 辅助
掌静脉识别因其静脉模式的独特性和皮下性质而成为一种高度安全的生物识别方式。然而，近红外光散射和传感器限制导致掌静脉图像对比度低，仍是重大挑战。为此，我们提出强度受限自适应对比度拉伸与双向高斯加权重叠块（ILACS-BGOT）方法，它是对先前开发的带分层高斯加权重叠块（ILACS-LGOT）技术的ILACS的增强。ILACS增强局部对比度，BGOT减轻块状伪影。本研究还将RootSIFT特征与KNN+RT集成，并纳入先前引入的均值和中位数距离（MMD）滤波器，以研究MMD和RT的参数变化及其对识别性能的影响。在三个基准数据集（CASIA、PolyU和PUT）上进行了全面分析，使用了42种MMD滤波器阈值和RT值的组合。结果用EER和准确率评估。结果表明，更大的模板尺寸可提高性能；不同的MMD阈值反映特定数据集的旋转变化。所提出的系统具有卓越的通用性，在EER和准确率方面均比现有方法有显著提高。此外，潜在的ILACS-BGOT机制表明其可能适用于除掌静脉识别之外的其他生物识别方式，如指静脉和掌纹识别，更广泛地适用于计算机视觉应用中的低对比度图像增强。
英文摘要
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.
