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

Domain-specific Learning of Multi-scale Facial Dynamics for Apparent Personality Traits Prediction