Facial Video based Detection of Physical Fatigue for Maximal Muscle Activity

Physical fatigue reveals the health condition of a person at for example health checkup, fitness assessment or rehabilitation training. This paper presents an efficient noncontact system for detecting non-localized physi-cal 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 utilizes a facial feature point tracking method by combining a ‘Good feature to track’ and a ‘Supervised descent method’ to address the challenges originates from realistic sce-nario. 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.