Hand Pose Classification Based on Neural Networks

TL;DR

This paper applies transfer learning with neural networks to classify hand presence and count in a hand-washing dataset, achieving perfect accuracy on a controlled dataset, and plans to extend to more complex models for video analysis.

Contribution

It demonstrates the use of transfer learning with simple neural networks for hand pose classification in a novel hand-washing dataset.

Findings

Achieved 100% accuracy on the controlled dataset.

Utilized transfer learning with a pre-trained model.

Plans to extend to dense models for video classification.

Abstract

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.