# Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand Hygiene

arXiv: https://arxiv.org/abs/2109.02917 · HTML: https://mlsanity.com/paper/2109.02917/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 46 references that are in the map.
- First posted on arXiv 7 Sep 2021.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Learning Representations by Maximizing Mutual Information Across Views](https://mlsanity.com/paper/1906.00910/) (2019)
- [S4L: Self-Supervised Semi-Supervised Learning](https://mlsanity.com/paper/1905.03670/) (2019)
- [Revisiting Self-Supervised Visual Representation Learning](https://mlsanity.com/paper/1901.09005/) (2019)
- [Self-supervised Co-training for Video Representation Learning](https://mlsanity.com/paper/2010.09709/) (2020)
- [Scaling and Benchmarking Self-Supervised Visual Representation Learning](https://mlsanity.com/paper/1905.01235/) (2019)
- [Boosting Self-Supervised Learning via Knowledge Transfer](https://mlsanity.com/paper/1805.00385/) (2018)
- [MS2L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition](https://mlsanity.com/paper/2010.05599/) (2020)
- [A neural network based on SPD manifold learning for skeleton-based hand gesture recognition](https://mlsanity.com/paper/1904.12970/) (2019)
- [Self-Supervised Learning For Few-Shot Image Classification](https://mlsanity.com/paper/1911.06045/) (2019)
- [Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition](https://mlsanity.com/paper/2007.05934/) (2020)
- [Improvements to context based self-supervised learning](https://mlsanity.com/paper/1711.06379/) (2017)
- [Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval](https://mlsanity.com/paper/1804.01223/) (2018)
- [Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention](https://mlsanity.com/paper/1907.08871/) (2019)
- [Make Skeleton-based Action Recognition Model Smaller, Faster and Better](https://mlsanity.com/paper/1907.09658/) (2019)
- [Extending and Analyzing Self-Supervised Learning Across Domains](https://mlsanity.com/paper/2004.11992/) (2020)
- [FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography](https://mlsanity.com/paper/2011.06104/) (2020)
- [CNN+RNN Depth and Skeleton based Dynamic Hand Gesture Recognition](https://mlsanity.com/paper/2007.11983/) (2020)
- [Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition](https://mlsanity.com/paper/1708.03278/) (2017)
- [Automated Quality Assessment of Hand Washing Using Deep Learning](https://mlsanity.com/paper/2011.11383/) (2020)
- [An Ensemble of Knowledge Sharing Models for Dynamic Hand Gesture Recognition](https://mlsanity.com/paper/2008.05732/) (2020)
- [A Prototype-Based Generalized Zero-Shot Learning Framework for Hand Gesture Recognition](https://mlsanity.com/paper/2009.13957/) (2020)
- [Synthetic Video Generation for Robust Hand Gesture Recognition in Augmented Reality Applications](https://mlsanity.com/paper/1911.01320/) (2019)
- [LE-HGR: A Lightweight and Efficient RGB-based Online Gesture Recognition Network for Embedded AR Devices](https://mlsanity.com/paper/2001.05654/) (2020)
- [3D dynamic hand gestures recognition using the Leap Motion sensor and convolutional neural networks](https://mlsanity.com/paper/2003.01450/) (2020)
- [Hand Gesture Recognition with Leap Motion](https://mlsanity.com/paper/1711.04293/) (2017)

## Built on by (most cited first, 1 of 1)

- [Hand Hygiene Assessment via Joint Step Segmentation and Key Action Scorer](https://mlsanity.com/paper/2209.12221/) (2022)

## Abstract (arXiv)

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.