Benchmarking the Explanatory Quality of Open-Weight Vision-Language Models in Face Recognition
Vision-Language Models (VLMs) have recently been proposed as promising tools for face recognition, as they can produce natural language explanations alongside similarity scores. This capability is considered appealing for face comparisons in forensic contexts, which require decisions to be transparent and auditable. However, existing evaluations of VLMs for that use case focus mostly on recognition accuracy, while the validity of generated explanations remains unquantified. In this work, we introduce a benchmarking framework for VLM-based face recognition that treats explanation quality as a core evaluation axis. We propose two criteria that explanations should satisfy: relevance, i.e., r...
| Authors | Laurent Colbois, Sébastien Marcel |
|---|---|
| Primary category | cs.CV |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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