TY - GEN
T1 - Detection of Mild Cognitive Impairment by Facial Videos
AU - Lee, Chien Cheng
AU - Chau, Hong Han Hank
AU - Wang, Hsiao Lun
AU - Chuang, Yi Fang
AU - Chau, Yawgeng
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this study, we proposed a two-stream ConvNet model to detect the mild cognitive impairment (MCI) using facial videos. The image frame containing the facial spatial information and the stacked optical flow fields containing the motion information were extracted from facial videos. Both were input to the two-stream CovnNet model to predict MCI. The experimental results showed that the validation accuracy reaches 91%. This finding indicates that an automatic, non-invasive, and inexpensive MCI screening methods from facial videos is feasible.
AB - In this study, we proposed a two-stream ConvNet model to detect the mild cognitive impairment (MCI) using facial videos. The image frame containing the facial spatial information and the stacked optical flow fields containing the motion information were extracted from facial videos. Both were input to the two-stream CovnNet model to predict MCI. The experimental results showed that the validation accuracy reaches 91%. This finding indicates that an automatic, non-invasive, and inexpensive MCI screening methods from facial videos is feasible.
UR - http://www.scopus.com/inward/record.url?scp=85138735689&partnerID=8YFLogxK
U2 - 10.1109/ICCE-Taiwan55306.2022.9869203
DO - 10.1109/ICCE-Taiwan55306.2022.9869203
M3 - Conference contribution
AN - SCOPUS:85138735689
T3 - Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
SP - 197
EP - 198
BT - Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
Y2 - 6 July 2022 through 8 July 2022
ER -
