SLAPBench: First Benchmark for MLLM-Based Four-Finger SLAP Fingerprint Verification
Researchers introduce SLAPBench, the first benchmark for evaluating multimodal large language models (MLLMs) on four-finger SLAP fingerprint verification, using data from NIST SD302b with 7,832 pairs. The study finds that prompting strategies determine whether verification collapses, while model capability affects discrimination performance. Claude Opus 4.8 achieves the best binary verification result (FAR=20.2%), and Qwen3-VL-8B achieves perfect separation (AUC=1.000) under similarity scoring, though this may reflect dataset artifacts rather than true capability.
Why it matters: This work establishes the first SLAP-specific MLLM baseline and reveals how prompting and model capability interact in high-stakes biometric verification tasks.
Full story at: arXiv AI/ML ↗