# Context-Aware Palmprint Recognition via a Relative Similarity Metric

arXiv: https://arxiv.org/abs/2504.11306 · HTML: https://mlsanity.com/paper/2504.11306/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [A Survey on Learning to Hash](https://mlsanity.com/paper/1606.00185/) (2016)
- [Simultaneous Feature Learning and Hash Coding with Deep Neural Networks](https://mlsanity.com/paper/1504.03410/) (2015)

## Abstract (arXiv)

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.