| dc.description.abstract | Nowadays, automatic deception detection has received considerable attention in the machine learning community owing to this research interest to its vast applications in the fields of social media, interviews, law enforcement, and the military. In this study, a novel deep convolution neural network (DCNN) named LieNet is proposed to precisely detect the multiscale variations of deception automatically. Our approach is a combination of contact and noncontact-based approaches. First, 20 frames from each video are fetched and concatenated to form a single image. Moreover, an audio signal is extracted from video and treated as image input by plotting the signal into 2-D plane. Furthermore, 13 channels of electroencephalogram signals are plotted into 2-D plane and concatenated to generate an image. Second, the LieNet model extracts features from each modality separately. Third, scores are estimated using a softmax classifier for all the modalities. Finally, three scores are combined using score level fusion to obtain a score, which gives support in favor of either deception or truth. The LieNet is validated on the “Bag-of-Lies (BoL),” “ real-life (RL) trail,” and “Miami University Deception Detection (MU3D)” databases by considering four evaluation indexes, viz., accuracy, precision, recall, and F1-score. Experimental outcomes depict that the LieNet defeats an initial work on Set-A and Set-B of the BoL database with average accuracies of 95.91% and 96.04%. respectively. The accuracies obtained by the LieNet are 97% and 98% on RL trail and MU3D databases respectively. | en_US |
