Open AccessFacial video‐based detection of physical fatigue for maximal muscle activity
Author(s) -
Haque Mohammad A.,
Irani Ramin,
Nasrollahi Kamal,
Moeslund Thomas B.
Publication year - 2016
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2015.0215
Subject(s) - computer science , artificial intelligence , computer vision , facial expression , feature (linguistics) , face (sociological concept) , feature extraction , face detection , facial muscles , pattern recognition (psychology) , facial motion capture , motion (physics) , video quality , point (geometry) , facial recognition system , mathematics , medicine , engineering , sociology , anatomy , social science , philosophy , linguistics , geometry , metric (unit) , operations management
Physical fatigue reveals the health condition of a person at, for example, health checkup, fitness assessment, or rehabilitation training. This study presents an efficient non‐contact system for detecting non‐localised physical fatigue from maximal muscle activity using facial videos acquired in a realistic environment with natural lighting where subjects were allowed to voluntarily move their head, change their facial expression, and vary their pose. The proposed method utilises a facial feature point tracking method by combining a ‘good feature to track’ and a ‘supervised descent method’ to address the challenges that originate from realistic scenario. A face quality assessment system was also incorporated in the proposed system to reduce erroneous results by discarding low quality faces that occurred in a video sequence due to problems in realistic lighting, head motion, and pose variation. Experimental results show that the proposed system outperforms video‐based existing system for physical fatigue detection.