FaceInsight: A Multimodal Large Language Model for Face Perception

Recent advances in multimodal large language models (MLLMs) have demonstrated strong capabilities in understanding general visual content. However, these general-domain MLLMs perform poorly in face perception tasks, often producing inaccurate or misleading responses to face-specific queries. To address this gap, we propose FaceInsight, a versatile face perception MLLM that provides fine-grained information. Our approach introduces visual textual alignment of facial knowledge to model both uncertain dependencies and deterministic relationships among facial information, mitigating the limitations of language-driven reasoning. Additionally, we incorporate face segmentation maps as an auxiliary perceptual modality, enriching visual input with localized structural cues to enhance semantic understanding. Comprehensive experiments show that FaceInsight consistently outperforms nine compared MLLMs under both training-free and fine-tuned settings.