  Fatigue detection is valued for people to keep mental health and prevent
safety accidents. However, detecting facial fatigue, especially mild fatigue in
the real world via machine vision is still a challenging issue due to lack of
non-lab dataset and well-defined algorithms. In order to improve the detection
capability on facial fatigue that can be used widely in daily life, this paper
provided an audiovisual dataset named DLFD (daily-life fatigue dataset) which
reflected people's facial fatigue state in the wild. A framework using
3D-ResNet along with non-local attention mechanism was training for extraction
of local and long-range features in spatial and temporal dimensions. Then, a
compacted loss function combining mean squared error and cross-entropy was
designed to predict both continuous and categorical fatigue degrees. Our
proposed framework has reached an average accuracy of 90.8% on validation set
and 72.5% on test set for binary classification, standing a good position
compared to other state-of-the-art methods. The analysis of feature map
visualization revealed that our framework captured facial dynamics and
attempted to build a connection with fatigue state. Our experimental results in
multiple metrics proved that our framework captured some typical, micro and
dynamic facial features along spatiotemporal dimensions, contributing to the
mild fatigue detection in the wild.
