Event correlation for deception detection in long video

Abstract

Automated Deception Detection (ADD) is a challenging task and still under study as a visual analysis task. Based on the idea that human micro-expressions and body movements could be used as clues for ADD, many works have proposed some action recognition models for extracting face and body spatiotemporal features. However, these features are not sufficient evidence for deception; moreover, micro-expressions are difficult to detect and real-life deception samples are hard to collect, thus ADD still has many challenges. In this paper, we present a global two-stream network (GTSN), which not only extracts face and body features, but also utilizes the correlation between the deceptions. GTSN can improve the accuracy of deception detection by adding historical information based on the correlation between the deceptions. We build a dataset named Deception-Truthful (DT) for evaluating the performance of our proposed model. Experimental results demonstrate that our GTSN model outperforms other action recognition models used for ADD. Further, the proposed GTSN model also performs well on the real trial videos widely used in ADD.