发表机构
Institute of Automation, CAS; Institute of Information Engineering, CAS; School of Cyber Security, UCAS; School of Artificial Intelligence, UCAS; Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, CAS; School of Computer Science and Engineering, the Faculty of Innovation Engineering, M.U.S.T（中国科学院自动化研究所; 中国科学院信息工程研究所; 中国科学院大学网络空间安全学院; 中国科学院大学人工智能学院; 中国科学院香港创新研究院人工智能与机器人中心; 澳门科技大学创新工程学院计算机科学与工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究从面部图像推断表面人格，提出GlanceFace框架，利用视觉语言模型、语义增强模块及不确定性感知学习策略，在基于MBTI的基准测试中表现出色，揭示面部特征与人格特质关系，助力具身智能体交互策略。
AI 中文摘要
在人机交互中，从面部图像推断表面人格对具身智能体在社交场景中很重要。与通过对话推断内在人格特质不同，此任务仅基于交互开始前的面部外观来模拟第一印象人格感知。现有研究主要集中在大五人格模型，且常依赖语言或多模态输入。对于在实践中广泛使用且更易被大语言模型解释的MBTI类型，仅面部线索能否支持与感知人格特质的有意义关联仍不明确。为此，我们提出GlanceFace，这是一个用于表面人格推断的端到端框架，利用视觉语言模型引入语义先验，通过语义增强面部表征模块捕捉细微人格相关线索，并采用不确定性感知学习策略处理噪声和主观标注。大量实验表明，该框架在基于MBTI的表面人格基准测试中表现出色，揭示了面部特征与感知人格特质之间的关系，突出了其支持具身智能体适应性初始交互策略的潜力。代码和数据集可在指定网址获取。
英文摘要
Inferring apparent personality from facial images is important in social scenarios for embodied agents in human-robot interaction. Unlike inferring intrinsic personality traits via conversation, this task models first-impression personality perception based solely on facial appearance before interaction begins. Existing studies mainly focus on the Big Five personality model and often rely on language or multimodal inputs. As a result, it remains unclear whether facial cues alone can support meaningful associations with perceived personality traits. This question is particularly relevant for MBTI types, which are widely used in practice and more readily interpretable by large language models. To this end, we propose \textbf{GlanceFace}, an end-to-end framework for apparent personality inference leveraging vision-language models to introduce semantic priors and a semantic-enhanced facial representation module to capture subtle personality-related cues, together with an uncertainty-aware learning strategy to handle noisy and subjective annotations. Extensive experiments demonstrate strong performance on MBTI-based apparent personality benchmarks and reveal relationships between facial characteristics and perceived personality traits, highlighting its potential to support adaptive initial interaction strategies for embodied agents. The code and dataset are available at https://github.com/MrHuan3/GlanceFace.
CommentsAccepted by The 9th Chinese Conference on Pattern Recognition and Computer Vision, PRCV2026
