  Deception detection has garnered increasing attention in recent years due to
the significant growth of digital media and heightened ethical and security
concerns. It has been extensively studied using multimodal methods, including
video, audio, and text. In addition, individual differences in deception
production and detection are believed to play a crucial role.Although some
studies have utilized individual information such as personality traits to
enhance the performance of deception detection, current systems remain limited,
partly due to a lack of sufficient datasets for evaluating performance. To
address this issue, we introduce a multimodal deception dataset MDPE. Besides
deception features, this dataset also includes individual differences
information in personality and emotional expression characteristics. It can
explore the impact of individual differences on deception behavior. It
comprises over 104 hours of deception and emotional videos from 193 subjects.
Furthermore, we conducted numerous experiments to provide valuable insights for
future deception detection research. MDPE not only supports deception
detection, but also provides conditions for tasks such as personality
recognition and emotion recognition, and can even study the relationships
between them. We believe that MDPE will become a valuable resource for
promoting research in the field of affective computing.
