SLAPBench: Benchmarking Multimodal Large Language Models for Four-Finger SLAP Fingerprint Verification

Researchers introduce SLAPBench, the first benchmark for evaluating multimodal large language models (MLLMs) on four-finger SLAP fingerprint verification, built from NIST SD302b with 7,832 pairs. Under similarity-scoring prompts, Qwen3-VL-8B achieves perfect separation (AUC = 1.000), while Claude Opus 4.8 reaches AUC = 0.953 and Gemma-3-12B AUC = 0.837, though the perfect score is treated as a diagnostic rather than capability due to potential near-duplicate detection.

arXiv:2607.15517v1 Announce Type: new Abstract: Four-finger SLAP fingerprints are flat live-scan impressions of the index, middle, ring, and little fingers of one hand, used for identity verification in border control and law enforcement. No benchmark has evaluated whether multimodal large language models MLLMs can verify identity from SLAP images. We introduce SLAPBench, the first benchmark for MLLM-based four-finger SLAP fingerprint verification, built from NIST SD302b with 7,832 pairs 176 mated, 7,656 non-mated . We evaluate four open-source MLLMs InternVL3-8B, Qwen2.5-VL-7B, Qwen3-VL-8B, Gemma-3-12B and the proprietary Claude Opus 4.8 under zero-shot, task-description, and similarity-scoring prompts. Prompting governs verification behavior. Task-description prompting collapses all four open-source models to near-100% False Accept Rate FAR , and Gemma-3-12B collapses under zero-shot as well; Claude Opus 4.8 alone resists collapse under both binary prompts, giving the best binary result FAR = 20.2% . Similarity scoring removes collapse across the open-source models and exposes wide capability gaps: Claude reaches AUC = 0.953 and Gemma-3-12B 0.837, while InternVL3-8B is inverted AUC = 0.590 and Qwen2.5-VL-7B near random 0.567 . Qwen3-VL-8B attains perfect separation AUC = 1.000 , which we treat as a diagnostic rather than as capability: SD302b holds one SLAP capture per finger position, so mated pairs are cross-resolution. A matched-resolution control leaves the perfect score intact, ruling out the resolution shortcut; what cannot be excluded within SD302b is near-duplicate detection, since a mated pair is one capture rendered twice. A fairness probe over gender, race, and age suggests disparity grows as discrimination weakens. SLAPBench establishes the first SLAP-specific MLLM baseline and shows that prompting governs collapse while model capability governs discrimination.