Can lies be faked? Comparing low-stakes and high-stakes deception video datasets from a Machine Learning perspective
-
Published:2024-09
Issue:
Volume:249
Page:123684
-
ISSN:0957-4174
-
Container-title:Expert Systems with Applications
-
language:en
-
Short-container-title:Expert Systems with Applications
Author:
Camara Mateus KarvatORCID, Postal AdrianaORCID, Maul Tomas HenriqueORCID, Paetzold Gustavo HenriqueORCID
Reference68 articles.
1. Amber, Faryal, Yousaf, Adeel, Imran, Muhammad, & Khurshid, Khurram (2019). P300 Based Deception Detection Using Convolutional Neural Network. In 2019 2nd international conference on communication, computing and digital systems (pp. 201–204). 2. Automatic deception detection in RGB videos using facial action units;Avola,2019 3. Baghel, Neeraj, Singh, Divyanshu, Dutta, Malay Kishore, Burget, Radim, & Myska, Vojtech (2020). Truth Identification from EEG Signal by using Convolution neural network: Lie Detection. In 2020 43rd international conference on telecommunications and signal processing (pp. 550–553). 4. Baltrušaitis, Tadas, Mahmoud, Marwa, & Robinson, Peter (2015). Cross-dataset learning and person-specific normalisation for automatic Action Unit detection. 06, In 2015 11th IEEE international conference and workshops on automatic face and gesture recognition (pp. 1–6). 5. Baltrušaitis, Tadas, Robinson, Peter, & Morency, Louis-Philippe (2016). OpenFace: An open source facial behavior analysis toolkit. In 2016 IEEE winter conference on applications of computer vision (pp. 1–10).
|
|