We propose a data-driven method for automatic deception detection in real-life trial data using visual and verbal cues. Using OpenFace with facial action unit recognition, we analyze the movement of facial features of the witness when posed with questions and the acoustic patterns using OpenSmile. We then perform a lexical analysis on the spoken words, emphasizing the use of pauses and utterance breaks, feeding that to a Support Vector Machine to test deceit or truth prediction. We then try out a method to incorporate utterance-based fusion of visual and lexical analysis, using string based matching.
Explainable Verbal Deception Detection using Transformers
National Technical University of Athens（雅典国立技术大学）；GESIS – Leibniz Institute for the Social Science（莱布尼茨社会科学研究所）；University College London（伦敦大学学院）；Tilburg University（蒂尔堡大学）
Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks
University of Michigan（密歇根大学）；Columbia University（哥伦比亚大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Saarland University of Applied Science（萨尔应用科学大学）
QUACK: Questioning, Understanding, and Auditing Communicated Knowledge in Multimodal Social Deduction Agents
McGill University（麦吉尔大学）；Mila - Quebec AI Institute（魁北克人工智能研究所）；University of Cambridge（剑桥大学）；MBZUAI - Mohamed bin Zayed University of Artificial Intelligence（MBZUAI - 摩苏尔·本·扎耶德人工智能大学）；University of Toronto（多伦多大学）；Salesforce
