Evaluating Multimodal Large Language Models for Heterogeneous Face Recognition

TL;DR

This paper systematically evaluates multimodal large language models for heterogeneous face recognition across various spectral modalities, revealing significant performance gaps compared to classical systems and highlighting current limitations.

Contribution

It provides a comprehensive benchmark of state-of-the-art MLLMs for cross-modality face recognition, emphasizing their limitations and the need for rigorous biometric evaluation.

Findings

MLLMs underperform classical face recognition in cross-spectral scenarios.

Significant performance gaps exist between MLLMs and traditional methods.

Current MLLMs are limited for practical heterogeneous face recognition applications.

Abstract

Multimodal Large Language Models (MLLMs) have recently demonstrated strong performance on a wide range of vision-language tasks, raising interest in their potential use for biometric applications. In this paper, we conduct a systematic evaluation of state-of-the-art MLLMs for heterogeneous face recognition (HFR), where enrollment and probe images are from different sensing modalities, including visual (VIS), near infrared (NIR), short-wave infrared (SWIR), and thermal camera. We benchmark multiple open-source MLLMs across several cross-modality scenarios, including VIS-NIR, VIS-SWIR, and VIS-THERMAL face recognition. The recognition performance of MLLMs is evaluated using biometric protocols and based on different metrics, including Acquire Rate, Equal Error Rate (EER), and True Accept Rate (TAR). Our results reveal substantial performance gaps between MLLMs and classical face recognition systems, particularly under challenging cross-spectral conditions, in spite of recent advances in MLLMs. Our findings highlight the limitations of current MLLMs for HFR and also the importance of rigorous biometric evaluation when considering their deployment in face recognition systems.