Skip to main content

Research Repository

Advanced Search

Deep learning driven multimodal fusion for automated deception detection

Gogate, M.; Adeel, A.; Hussain, A.


A. Adeel


Humans ability to detect lies is no more accurate than chance according to the American Psychological Association. The state-of-the-art deception detection methods, such as deception detection stem from early theories and polygraph have proven to be unreliable. Recent advancement in deception detection includes the application of advanced data analysis and machine learning algorithms. This paper presents a novel deep learning driven multimodal fusion for automated deception detection, incorporating audio cues for the first time along with the visual and textual cues. The critical analysis and comparison of the proposed deep convolutional neural network (CNN) based approach with the state-of-the-art multimodal fusion methods have revealed significant performance improvement up to 96% as compared to the 82% prediction accuracy reported in the recent literature.

Presentation Conference Type Conference Paper (Published)
Conference Name 2017 IEEE Symposium Series on Computational Intelligence (SSCI)
Start Date Nov 27, 2017
End Date Dec 1, 2017
Online Publication Date Feb 8, 2018
Publication Date 2018
Deposit Date Sep 27, 2019
Publisher Institute of Electrical and Electronics Engineers
Public URL