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Automated systems that detect the social behavior of deception can enhancehuman well-being across medical, social work, and legal domains. Labeleddatasets to train supervised deception detection models can rarely be collectedfor real-world, high-stakes contexts. To address this challenge, we propose thefirst unsupervised approach for detecting real-world, high-stakes deception invideos without requiring labels. This paper presents our novel approach foraffect-aware unsupervised Deep Belief Networks (DBN) to learn discriminativerepresentations of deceptive and truthful behavior. Drawing on psychologytheories that link affect and deception, we experimented with unimodal andmultimodal DBN-based approaches trained on facial valence, facial arousal,audio, and visual features. In addition to using facial affect as a feature onwhich DBN models are trained, we also introduce a DBN training procedure thatuses facial affect as an aligner of audio-visual representations. We conductedclassification experiments with unsupervised Gaussian Mixture Model clusteringto evaluate our approaches. Our best unsupervised approach (trained on facialvalence and visual features) achieved an AUC of 80%, outperforming humanability and performing comparably to fully-supervised models. Our resultsmotivate future work on unsupervised, affect-aware computational approaches fordetecting deception and other social behaviors in the wild.

Affect-Aware Deep Belief Network Representations for Multimodal Unsupervised Deception Detection