There is growing interest in the use of automated psychological profiling systems, specifically applying machine learning to the field of deception detection. Several psychological studies and machine-based models have been reporting the use of eye interaction, gaze and facial movements as important clues to deception detection. However, the identification of very specific and distinctive features is still required. For the first time, we investigate the fine-grained level eyes and facial micro-movements to identify the distinctive features that provide significant clues for the automated deception detection. A real-time deception detection approach was developed utilizing advanced computer vision and machine learning approaches to model the non-verbal deceptive behavior. Artificial neural networks, random forests and support vector machines were selected as base models for the data on the total of 262,000 discrete measurements with 1,26,291 and 128,735 of deceptive and truthful instances, respectively. The data set used in this study is part of an ongoing programme to collect a larger dataset on the effects of gender and ethnicity on deception detection. Some observations are made based on this data which should not be interpreted as scientific conclusions, but pointers for future work. Analysis of the above models revealed that eye movements carry relatively important clues to distinguish truthful and deceptive behaviours. The research outcomes align with the findings from forensic psychologists who also reported the eye movements as distinctive for the truthful and deceptive behavior. The research outcomes and proposed approach are beneficial for human experts and has many applications within interdisciplinary domains.

人们对自动心理分析系统的使用越来越感兴趣，特别是将机器学习应用于欺骗检测领域。一些心理学研究和基于机器的模型已经报告了将眼睛互动，凝视和面部运动作为欺骗检测的重要线索的使用。然而，仍然需要识别非常具体和独特的特征。第一次，我们调查了细粒度的眼睛和面部微动，以识别为自动欺骗检测提供重要线索的独特功能。利用高级计算机视觉和机器学习方法开发了一种实时欺骗检测方法，以对非语言欺骗行为进行建模。人工神经网络，选择了随机森林和支持向量机作为基础模型，用于总共262,000次离散测量的数据，分别有1,26,291和128,735个欺骗和真实的实例。本研究中使用的数据集是正在进行的计划的一部分，该计划旨在收集有关性别和种族对欺骗检测的影响的较大数据集。基于这些数据得出的一些观察结果不应被解释为科学结论，而应作为未来工作的指针。对以上模型的分析表明，眼球运动带有相对重要的线索以区分真实和欺骗行为。研究结果与法医心理学家的发现一致，法医心理学家还报道眼球运动是真实和欺骗行为的独特特征。