FPBench: A Comprehensive Benchmark of Multimodal Large Language Models for Fingerprint Analysis

Multimodal LLMs (MLLMs) have gained significant traction in complex data analysis, visual question answering, generation, and reasoning. Recently, they have been used for analyzing the biometric utility of iris and face images. However, their capabilities in fingerprint understanding are yet unexplored. In this work, we design a comprehensive benchmark, \textsc{FPBench} that evaluates the performance of 20 MLLMs (open-source and proprietary) across 7 real and synthetic datasets on 8 biometric and forensic tasks using zero-shot and chain-of-thought prompting strategies. We discuss our findings in terms of performance, explainability and share our insights into the challenges and limitations. We establish \textsc{FPBench} as the first comprehensive benchmark for fingerprint domain understanding with MLLMs paving the path for foundation models for fingerprints.

DiffClean: Diffusion-based Makeup Removal for Accurate Age Estimation

Accurate age verification can protect underage users from unauthorized access to online platforms and e-commerce sites that provide age-restricted services. However, accurate age estimation can be confounded by several factors, including facial makeup that can induce changes to alter perceived identity and age to fool both humans and machines. In this work, we propose DiffClean which erases makeup traces using a text-guided diffusion model to defend against makeup attacks. DiffClean improves age estimation (minor vs. adult accuracy by 4.8%) and face verification (TMR by 8.9% at FMR=0.01%) over competing baselines on digitally simulated and real makeup images.