LocalScore: Local Density-Aware Similarity Scoring for Biometrics
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866912866752266240 |
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| author | Su, Yiyang Kim, Minchul Zhu, Jie Perry, Christopher Liu, Feng Jain, Anil Liu, Xiaoming |
| author_facet | Su, Yiyang Kim, Minchul Zhu, Jie Perry, Christopher Liu, Feng Jain, Anil Liu, Xiaoming |
| contents | Open-set biometrics faces challenges with probe subjects who may not be enrolled in the gallery, as traditional biometric systems struggle to detect these non-mated probes. Despite the growing prevalence of multi-sample galleries in real-world deployments, most existing methods collapse intra-subject variability into a single global representation, leading to suboptimal decision boundaries and poor open-set robustness. To address this issue, we propose LocalScore, a simple yet effective scoring algorithm that explicitly incorporates the local density of the gallery feature distribution using the k-th nearest neighbors. LocalScore is architecture-agnostic, loss-independent, and incurs negligible computational overhead, making it a plug-and-play solution for existing biometric systems. Extensive experiments across multiple modalities demonstrate that LocalScore consistently achieves substantial gains in open-set retrieval (FNIR@FPIR reduced from 53% to 40%) and verification (TAR@FAR improved from 51% to 74%). We further provide theoretical analysis and empirical validation explaining when and why the method achieves the most significant gains based on dataset characteristics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01012 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LocalScore: Local Density-Aware Similarity Scoring for Biometrics Su, Yiyang Kim, Minchul Zhu, Jie Perry, Christopher Liu, Feng Jain, Anil Liu, Xiaoming Computer Vision and Pattern Recognition Open-set biometrics faces challenges with probe subjects who may not be enrolled in the gallery, as traditional biometric systems struggle to detect these non-mated probes. Despite the growing prevalence of multi-sample galleries in real-world deployments, most existing methods collapse intra-subject variability into a single global representation, leading to suboptimal decision boundaries and poor open-set robustness. To address this issue, we propose LocalScore, a simple yet effective scoring algorithm that explicitly incorporates the local density of the gallery feature distribution using the k-th nearest neighbors. LocalScore is architecture-agnostic, loss-independent, and incurs negligible computational overhead, making it a plug-and-play solution for existing biometric systems. Extensive experiments across multiple modalities demonstrate that LocalScore consistently achieves substantial gains in open-set retrieval (FNIR@FPIR reduced from 53% to 40%) and verification (TAR@FAR improved from 51% to 74%). We further provide theoretical analysis and empirical validation explaining when and why the method achieves the most significant gains based on dataset characteristics. |
| title | LocalScore: Local Density-Aware Similarity Scoring for Biometrics |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.01012 |