FaceXBench introduces a groundbreaking evaluation framework for assessing how well multimodal large language models understand and analyze human faces.

• Includes 5,000 multimodal multiple-choice questions from 25 datasets plus a new custom dataset
• Systematically evaluates MLLMs on complex face-related tasks including authentication and recognition
• Establishes critical benchmarks for facial analysis capabilities in modern AI systems
• Directly addresses security implications for biometric authentication systems

This research is particularly significant for the security industry, as it provides the first standardized way to evaluate how AI systems process and interpret facial data—a core component of modern biometric security solutions.

FaceXBench: Evaluating Multimodal LLMs on Face Understanding