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Deception detection is an important task that has been a hot research topicdue to its potential applications. It can be applied in many areas, fromnational security (e.g., airport security, jurisprudence, and law enforcement)to real-life applications (e.g., business and computer vision). However, somecritical problems still exist and are worth more investigation. One of thesignificant challenges in the deception detection tasks is the data scarcityproblem. Until now, only one multi-modal benchmark open dataset for humandeception detection has been released, which contains 121 video clips fordeception detection (i.e., 61 for deceptive class and 60 for truthful class).Such an amount of data is hard to drive deep neural network-based methods.Hence, those existing models often suffer from overfitting problems and lowgeneralization ability. Moreover, the ground truth data contains some unusableframes for many factors. However, most of the literature did not pay attentionto these problems. Therefore, in this paper, we design a series of datapreprocessing methods to deal with the aforementioned problem first. Then, wepropose a multi-modal deception detection framework to construct our novelemotional state-based feature and use the open toolkit openSMILE to extract thefeatures from the audio modality. We also design a voting scheme to combine theemotional states information obtained from visual and audio modalities.Finally, we can determine the novel emotion state transformation feature withour self-designed algorithms. In the experiment, we conduct the criticalanalysis and comparison of the proposed methods with the state-of-the-artmulti-modal deception detection methods. The experimental results show that theoverall performance of multi-modal deception detection has a significantimprovement in the accuracy from 87.77% to 92.78% and the ROC-AUC from 0.9221to 0.9265.

Constructing Robust Emotional State-based Feature with a Novel Voting Scheme for Multi-modal Deception Detection in Videos