Context-Aware Palmprint Recognition via a Relative Similarity Metric
This addresses the problem of false positives and negatives in biometric security systems for palmprint recognition, representing an incremental improvement over existing methods.
The paper tackled the problem of palmprint recognition by introducing a Relative Similarity Metric (RSM) to improve matching robustness, achieving a state-of-the-art 0.000036% Equal Error Rate on the Tongji dataset.
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.