A Deep Learning Approach for Multimodal Deception Detection

03/01/2018

∙

by   Gangeshwar Krishnamurthy, et al.

∙

0

∙

share

Automatic deception detection is an important task that has gained momentum in computational linguistics due to its potential applications. In this paper, we propose a simple yet tough to beat multi-modal neural model for deception detection. By combining features from different modalities such as video, audio, and text along with Micro-Expression features, we show that detecting deception in real life videos can be more accurate. Experimental results on a dataset of real-life deception videos show that our model outperforms existing techniques for deception detection with an accuracy of 96.14 0.9799.