Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand Hygiene
This work addresses data mismatch and fine-grained action recognition for hand hygiene monitoring, but it is incremental as it primarily focuses on dataset creation with a minor method recommendation.
The paper tackles the problem of fine-grained hand gesture recognition in hand hygiene systems by introducing a new high-quality dataset (MFH) with 731,147 samples from multiple viewpoints, and shows that a self-supervised learning method yields competitive performance in accuracy and Macro F1-score.
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.