Automated systems that detect the social behavior of deception can enhance human well-being across medical, social work, and legal domains. Labeled datasets to train supervised deception detection models can rarely be collected for real-world, high-stakes contexts. To address this challenge, we propose the first unsupervised approach for detecting real-world, high-stakes deception in videos without requiring labels. This paper presents our novel approach for affect-aware unsupervised Deep Belief Networks (DBN) to learn discriminative representations of deceptive and truthful behavior. Drawing on psychology theories that link affect and deception, we experimented with unimodal and multimodal DBN-based approaches trained on facial valence, facial arousal, audio, and visual features. In addition to using facial affect as a feature on which DBN models are trained, we also introduce a DBN training procedure that uses facial affect as an aligner of audio-visual representations. We conducted classification experiments with unsupervised Gaussian Mixture Model clustering to evaluate our approaches. Our best unsupervised approach (trained on facial valence and visual features) achieved an AUC of 80%, outperforming human ability and performing comparably to fully-supervised models. Our results motivate future work on unsupervised, affect-aware computational approaches for detecting deception and other social behaviors in the wild.
翻译:检测欺骗的社会行为的自动化系统可以提高医学、社会工作和法律领域的人类福祉。 用于培训受监督的欺骗检测模型的标签数据集很少能用于真实世界、高摄取环境。 为了应对这一挑战,我们提出了第一个未经监督的检测真实世界、高摄取视频中欺骗而不需贴标签的方法。 本文介绍了我们用于影响觉悟的、不受监督的深海信仰网络的新颖方法,以学习欺骗和真实行为的歧视性表现。 借鉴影响和欺骗联系的心理学理论,我们实验了在面部价值、面部振奋、听力和视觉特征方面受过培训的单式和多式联运的DBN方法。 除了使用面部影响作为DBN模型培训的特征外,我们还引入了DBN培训程序,将面部影响用作视听表现的匹配者。 我们用未经监督的Gausian Mixturextur模型组合进行了分类实验,以评价我们的方法。 我们最好的未经监督的DBNB方法(在面部价值和视觉能力方面进行了训练),并展示了我们未来的视觉模型, 完成了我们80个前期的自我探测和视觉分析模型。