# Hand Pose Classification Based on Neural Networks

arXiv: https://arxiv.org/abs/2108.04529 · HTML: https://mlsanity.com/paper/2108.04529/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 10 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Deep Photo Style Transfer](https://mlsanity.com/paper/1703.07511/) (2017)
- [Visually Indicated Sounds](https://mlsanity.com/paper/1512.08512/) (2015)
- [Reading Text in the Wild with Convolutional Neural Networks](https://mlsanity.com/paper/1412.1842/) (2014)
- [Transfer Learning for Video Recognition with Scarce Training Data for Deep Convolutional Neural Network](https://mlsanity.com/paper/1409.4127/) (2014)

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

- [A Deep Learning Based Automated Hand Hygiene Training System](https://mlsanity.com/paper/2112.05667/) (2021)

## Abstract (arXiv)

In this work, deep learning models are applied to a segment of a robust hand-washing dataset that has been created with the help of 30 volunteers. This work demonstrates the classification of presence of one hand, two hands and no hand in the scene based on transfer learning. The pre-trained model; simplest NN from Keras library is utilized to train the network with 704 images of hand gestures and the predictions are carried out for the input image. Due to the controlled and restricted dataset, 100% accuracy is achieved during the training with correct predictions for the input image. Complete handwashing dataset with dense models such as AlexNet for video classification for hand hygiene stages will be used in the future work.