用相对相似度提升掌纹识别准确率，有效减少误判。
Context-Aware Palmprint Recognition via a Relative Similarity Metric
- 引入相对相似度度量，比较样本间相似性在全局中的位置。
- 在同济数据集上实现0.000036%的错误率，创当前最优。
- 适合需要高精度生物识别的场景，如安防与金融认证。
我们提出一种新的掌纹匹配机制，通过引入相对相似度度量（RSM），增强现有匹配框架的鲁棒性和区分能力。传统系统依赖于余弦或欧氏距离等直接成对相似度度量，但这些方法无法捕捉成对相似度在整体数据集中的相对位置。本文方法通过评估相似度得分在全部身份间的相对一致性，更有效地抑制假阳性与假阴性。该方法基于CCNet架构，在同济数据集上实现0.000036%的等错误率（EER），超越此前方法，验证了在掌纹匹配中融入关系结构的有效性。
原文摘要 · Abstract (English)
We propose a new approach to matching mechanism for palmprint recognition by introducing a Relative Similarity Metric (RSM) that enhances the robustness and discriminability of existing matching frameworks. While conventional systems rely on direct pairwise similarity measures, such as cosine or Euclidean distances, these metrics fail to capture how a pairwise similarity compares within the context of the entire dataset. Our method addresses this by evaluating the relative consistency of similarity scores across up to all identities, allowing for better suppression of false positives and negatives. Applied atop the CCNet architecture, our method achieves a new state-of-the-art 0.000036% Equal Error Rate (EER) on the Tongji dataset, outperforming previous methods and demonstrating the efficacy of incorporating relational structure into the palmprint matching process.
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