Human personality decides various aspects of their daily life and working behaviors. Since personality traits are relatively stable over time and unique for each subject, previous approaches frequently infer personality from a single frame or short-term behaviors. Moreover, most of them failed to specifically extract person-specific and unique cues for personality recognition. In this paper, we propose a novel video-based automatic personality traits recognition approach which consists of: (1) a \textbf{domain-specific facial behavior modelling} module that extracts personality-related multi-scale short-term human facial behavior features; (2) a \textbf{long-term behavior modelling} module that summarizes all short-term features of a video as a long-term/video-level personality representation and (3) a \textbf{multi-task personality traits prediction module} that models underlying relationship among all traits and jointly predict them based on the video-level personality representation. We conducted the experiments on ChaLearn First Impression dataset, and our approach achieved comparable results to the state-of-the-art. Importantly, we show that all three proposed modules brought important benefits for personality recognition.

翻译：人的个性决定其日常生活和工作行为的各个方面。由于个性特征随着时间而相对稳定,每个学科都有其独特性,以往的方法经常从单一框架或短期行为中推断个性。此外,大多数方法没有具体地提取个性识别的特有提示。在本文中,我们建议采用新型的视频自动个性识别方法,其中包括:(1) textbf{domaphine Associal model} 模块,其中提取与个性有关的多尺度短期人类面部行为特征;(2) textb{long-roundal 行为建模模块,其中概述了作为长期/视频级别个性描述的视频的所有短期特征;(3) 一种\ textbf{ multi-task 个性特征预测模块。}该模块以所有特征之间的关系为基础,并根据视频级别的个性描述共同预测这些特征。我们在ChaLearn First Imppression数据集上进行了实验,我们的方法取得了与状态相近的结果。Ny,我们展示了所有三个拟议的模块都为人格识别带来重要的惠益。