FaceXBench: Evaluating Multimodal LLMs on Face Understanding

Abstract

Multimodal Large Language Models (MLLMs) demonstrate impressive problem-solving abilities across a wide range of tasks and domains. However, their capacity for face understanding has not been systematically studied. To address this gap, we introduce FaceXBench, a comprehensive benchmark designed to evaluate MLLMs on complex face understanding tasks. FaceXBench includes 5,000 multimodal multiple-choice questions derived from 25 public datasets and a newly created dataset, FaceXAPI. These questions cover 14 tasks across 6 broad categories, assessing MLLMs’ face understanding abilities in bias and fairness, face authentication, recognition, analysis, localization and tool retrieval. Using FaceXBench, we conduct an extensive evaluation of 26 open-source MLLMs alongside 2 proprietary models, revealing the unique challenges in complex face understanding tasks. We analyze the models across three evaluation settings: zero-shot, in-context task description, and chain-of-thought prompting. Our detailed analysis reveals that current MLLMs, including advanced models like GPT-4o, and GeminiPro 1.5, show significant room for improvement. We believe FaceXBench will be a crucial resource for developing MLLMs equipped to perform sophisticated face understanding.