MDPE: A Multimodal Deception Dataset with Personality and Emotional Characteristics
This provides a new dataset for researchers in affective computing to study deception detection, personality recognition, and emotion recognition, though it is incremental as it builds on existing multimodal approaches.
The authors tackled the lack of datasets for evaluating deception detection by introducing MDPE, a multimodal dataset with over 104 hours of videos from 193 subjects that includes personality and emotional characteristics, enabling exploration of individual differences in deception behavior.
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.