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

TL;DR

This paper introduces an unsupervised affect-aware deep learning method using Deep Belief Networks to detect deception in videos without labeled data, achieving high accuracy and outperforming humans.

Contribution

It presents the first unsupervised affect-aware DBN approach for deception detection, leveraging multimodal features and psychological theories, with promising results in real-world scenarios.

Findings

Achieved 80% AUC in deception detection

Outperformed human deception detection ability

Comparable to supervised models in accuracy

Abstract

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.