Deception detection is a relevant ability in high stakes situations such as police interrogatories or court trials, where the outcome is highly influenced by the interviewed person behavior. With the use of specific devices, e.g. polygraph or magnetic resonance, the subject is aware of being monitored and can change his behavior, thus compromising the interrogation result. For this reason, video analysis-based methods for automatic deception detection are receiving ever increasing interest. In this paper, a deception detection approach based on RGB videos, leveraging both facial features and stacked generalization ensemble, is proposed. First, a face, which is well-known to present several meaningful cues for deception detection, is identified, aligned, and masked to build video signatures. These signatures are constructed starting from five different descriptors, which allow the system to capture both static and dynamic facial characteristics. Then, video signatures are given as input to four base-level algorithms, which are subsequently fused applying the stacked generalization technique, resulting in a more robust meta-level classifier used to predict deception. By exploiting relevant cues via specific features, the proposed system achieves improved performances on a public dataset of famous court trials, with respect to other state-of-the-art methods based on facial features, highlighting the effectiveness of the proposed method.

中文翻译：

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LieToMe：一种从面部线索进行欺骗检测的集成方法

欺骗检测是在高风险情况下的相关能力，例如警察审讯或法庭审判，其结果受到受访者行为的高度影响。通过使用特定设备，例如测谎仪或磁共振，受试者意识到被监控并可以改变他的行为，从而影响审讯结果。出于这个原因，基于视频分析的自动欺骗检测方法越来越受到关注。在本文中，提出了一种基于 RGB 视频的欺骗检测方法，同时利用面部特征和堆叠泛化集成。首先，识别、对齐和遮罩以构建视频签名的人脸，众所周知，该人脸可以为欺骗检测提供几个有意义的线索。这些签名是从五个不同的描述符开始构建的，这些描述符允许系统捕获静态和动态的面部特征。然后，视频签名作为四个基本级别算法的输入，随后应用堆叠泛化技术进行融合，从而产生更强大的元级分类器，用于预测欺骗。通过通过特定特征利用相关线索，相对于其他基于面部特征的最新方法，所提出的系统在著名法庭审判的公共数据集上实现了改进的性能，突出了所提出方法的有效性。随后应用堆叠泛化技术进行融合，从而产生更强大的元级分类器，用于预测欺骗。通过通过特定特征利用相关线索，相对于其他基于面部特征的最新方法，所提出的系统在著名法庭审判的公共数据集上实现了改进的性能，突出了所提出方法的有效性。随后应用堆叠泛化技术进行融合，从而产生更强大的元级分类器，用于预测欺骗。通过通过特定特征利用相关线索，相对于其他基于面部特征的最新方法，所提出的系统在著名法庭审判的公共数据集上实现了改进的性能，突出了所提出方法的有效性。