论文标题
对明显人格特征预测的多尺度面部动态的特定领域学习
Domain-specific Learning of Multi-scale Facial Dynamics for Apparent Personality Traits Prediction
论文作者
论文摘要
人格决定了日常生活的各个方面和工作行为。由于人格特征随着时间的流逝而相对稳定，并且对于每个主题而言是独一无二的，因此以前的方法经常从单一框架或短期行为推断性格。此外，他们中的大多数未能专门提取人格识别的特定人物和独特的线索。在本文中，我们提出了一种基于视频的新型自动人格特征识别方法，该方法包括：（1）A \ TextBf {域特异性面部行为模型}模块，该模块提取了与人格相关的多规模短期人类面部行为特征； （2）a \ textbf {长期行为建模}模块，该模块总结了视频的所有短期特征作为长期/视频级别的个性表示，以及（3）a \ textbf {多任务个性人格特质预测模块}，该模型在所有特征之间进行了模型，并基于视频层面的表现来预测它们。我们在Chalearn First Ampression数据集上进行了实验，我们的方法与最先进的结果相当。重要的是，我们表明所有三个提议的模块都为人格认可带来了重要的好处。
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.
