  This paper contributes a new high-quality dataset for hand gesture
recognition in hand hygiene systems, named "MFH". Generally, current datasets
are not focused on: (i) fine-grained actions; and (ii) data mismatch between
different viewpoints, which are available under realistic settings. To address
the aforementioned issues, the MFH dataset is proposed to contain a total of
731147 samples obtained by different camera views in 6 non-overlapping
locations. Additionally, each sample belongs to one of seven steps introduced
by the World Health Organization (WHO). As a minor contribution, inspired by
advances in fine-grained image recognition and distribution adaptation, this
paper recommends using the self-supervised learning method to handle these
preceding problems. The extensive experiments on the benchmarking MFH dataset
show that the introduced method yields competitive performance in both the
Accuracy and the Macro F1-score. The code and the MFH dataset are available at
https://github.com/willogy-team/hand-gesture-recognition-smc2021.
