TY - GEN
T1 - Discerning Genuine and Acted Smiles Using Neural Networks
AU - Moussa, Mostafa
AU - Tariq, Usman
AU - Al-Nashash, Hasan
AU - Al-Shargie, Fares
N1 - Funding Information:
ACKNOWLEDGMENT We acknowledge and thank Biosciences and Bioengineering Research Institute for its support of this work, as well as the Department of Electrical Engineering, American University of Sharjah, Sharjah, UAE. The work is partially funded by the research grants EFRG18-BBR-CEN-02 and EFRG-EN0244, awarded by the American University of Sharjah, Sharjah, UAE
Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Deception recognition is a growing area of interest in studies involving electroencephalograms (EEGs), as it paves the way for further research in demystifying and understanding the human brain. Furthermore, it has other practical applications that can prove to be beneficial to several fields of research and industry. A part of deception recognition is identifying fake smiles and true smiles given in response to certain stimuli. Here, we develop a neural network to predict whether a smile is fake simply by going through the EEG signals, given an example dataset. The network used, obtained an average accuracy of 72.38 % before principle component analysis was applied, and 78.29 % after for all subjects. In addition, the average sensitivity for all subjects was 77.35 % and the average specificity was 88.29 %.
AB - Deception recognition is a growing area of interest in studies involving electroencephalograms (EEGs), as it paves the way for further research in demystifying and understanding the human brain. Furthermore, it has other practical applications that can prove to be beneficial to several fields of research and industry. A part of deception recognition is identifying fake smiles and true smiles given in response to certain stimuli. Here, we develop a neural network to predict whether a smile is fake simply by going through the EEG signals, given an example dataset. The network used, obtained an average accuracy of 72.38 % before principle component analysis was applied, and 78.29 % after for all subjects. In addition, the average sensitivity for all subjects was 77.35 % and the average specificity was 88.29 %.
KW - artificial neural networks
KW - deep learning
KW - Electroencephalogram (EEG)
KW - Emotion
UR - http://www.scopus.com/inward/record.url?scp=85081364223&partnerID=8YFLogxK
U2 - 10.1109/ISSPIT47144.2019.9001848
DO - 10.1109/ISSPIT47144.2019.9001848
M3 - Conference contribution
AN - SCOPUS:85081364223
T3 - 2019 IEEE 19th International Symposium on Signal Processing and Information Technology, ISSPIT 2019
BT - 2019 IEEE 19th International Symposium on Signal Processing and Information Technology, ISSPIT 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2019
Y2 - 10 December 2019 through 12 December 2019
ER -