SLAPBench: Benchmarking Multimodal Large Language Models for Four-Finger SLAP Fingerprint Verification
Quick Answer
SLAPBench introduces the first benchmark for verifying four-finger SLAP fingerprints using multimodal large language models (MLLMs).
Quick Take
Evaluations show that Claude Opus 4.8 achieves the best binary result with a 20.2% False Accept Rate, while Qwen3-VL-8B attains perfect separation (AUC = 1.000). The study highlights the impact of prompting on model performance and fairness disparities across demographics.
Key Points
- SLAPBench is built from NIST SD302b with 7,832 fingerprint pairs.
- Task-description prompting leads to nearly 100% False Accept Rate in open-source models.
- Claude Opus 4.8 shows the best performance with a 20.2% FAR.
- Qwen3-VL-8B achieves perfect separation (AUC = 1.000) but is diagnostic.
- Fairness analysis reveals growing disparities as discrimination weakens.
DeepSignal Analysis
What happened
SLAPBench is introduced as the first benchmark for verifying four-finger SLAP fingerprints using multimodal large language models (MLLMs). Evaluations reveal that Claude Opus 4.8 achieves a 20.2% False Accept Rate, while Qwen3-VL-8B shows perfect separation with an AUC of 1.000. The study emphasizes the role of prompting in model performance and fairness disparities.
Key evidence
- SLAPBench evaluates four open-source MLLMs and Claude Opus 4.8 using a dataset of 7,832 fingerprint pairs, consisting of 176 mated and 7,656 non-mated pairs.
- Under task-description prompting, all four open-source models exhibit near-100% False Accept Rate, while Claude Opus 4.8 maintains a 20.2% False Accept Rate.
- Qwen3-VL-8B achieves an AUC of 1.000, indicating perfect separation, but this is attributed to the dataset's structure rather than inherent model capability.
Why it matters
The introduction of SLAPBench provides a foundational benchmark for assessing MLLMs in the context of fingerprint verification, an area previously unexplored. The findings highlight significant performance variations among models, particularly under different prompting strategies. Understanding these disparities is crucial for developing fair and effective identity verification systems, especially in sensitive applications like border control and law enforcement.
Paper Resources
Source Excerpt
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 (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-s
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