We present a system for covert automated deception detection in real-life
courtroom trial videos. We study the importance of different modalities like
vision, audio and text for this task. On the vision side, our system uses
classifiers trained on low level video features which predict human
micro-expressions. We show that predictions of high-level micro-expressions can
be used as features for deception prediction. Surprisingly, IDT (Improved Dense
Trajectory) features which have been widely used for action recognition, are
also very good at predicting deception in videos. We fuse the score of
classifiers trained on IDT features and high-level micro-expressions to improve
performance. MFCC (Mel-frequency Cepstral Coefficients) features from the audio
domain also provide a significant boost in performance, while information from
transcripts is not very beneficial for our system. Using various classifiers,
our automated system obtains an AUC of 0.877 (10-fold cross-validation) when
evaluated on subjects which were not part of the training set. Even though
state-of-the-art methods use human annotations of micro-expressions for
deception detection, our fully automated approach outperforms them by 5%. When
combined with human annotations of micro-expressions, our AUC improves to
0.922. We also present results of a user-study to analyze how well do average
humans perform on this task, what modalities they use for deception detection
and how they perform if only one modality is accessible. Our project page can
be found at https://doubaibai.github.io/DARE/.

提出一种针对真实法庭审判视频中隐蔽自动欺诈检测的系统，该系统利用从图像、声音和文本三个层面得到的低级和高级特征预测人类微表情，其中，IDT 特征 和 MFCC 特征也对系统性能提供了重要帮助，系统在经过交叉验证的数据集上的 AUC 为 0.877，优于现有接近最优的方法，同时也提供了一个用户研究，分析一般人在这个任务上的表现。