# A Novel Hand Gesture Detection and Recognition system based on ensemble-based Convolutional Neural Network

arXiv: https://arxiv.org/abs/2202.12519 · HTML: https://mlsanity.com/paper/2202.12519/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [A Real-time Hand Gesture Recognition and Human-Computer Interaction System](https://mlsanity.com/paper/1704.07296/) (2017)

## Built on by (most cited first, 2 of 2)

- [Deep learning based Hand gesture recognition system and design of a Human-Machine Interface](https://mlsanity.com/paper/2207.03112/) (2022)
- [Novel Human Machine Interface via Robust Hand Gesture Recognition System using Channel Pruned YOLOv5s Model](https://mlsanity.com/paper/2407.02585/) (2024)

## Abstract (arXiv)

Nowadays, hand gesture recognition has become an alternative for human-machine interaction. It has covered a large area of applications like 3D game technology, sign language interpreting, VR (virtual reality) environment, and robotics. But detection of the hand portion has become a challenging task in computer vision and pattern recognition communities. Deep learning algorithm like convolutional neural network (CNN) architecture has become a very popular choice for classification tasks, but CNN architectures suffer from some problems like high variance during prediction, overfitting problem and also prediction errors. To overcome these problems, an ensemble of CNN-based approaches is presented in this paper. Firstly, the gesture portion is detected by using the background separation method based on binary thresholding. After that, the contour portion is extracted, and the hand region is segmented. Then, the images have been resized and fed into three individual CNN models to train them in parallel. In the last part, the output scores of CNN models are averaged to construct an optimal ensemble model for the final prediction. Two publicly available datasets (labeled as Dataset-1 and Dataset-2) containing infrared images and one self-constructed dataset have been used to validate the proposed system. Experimental results are compared with the existing state-of-the-art approaches, and it is observed that our proposed ensemble model outperforms other existing proposed methods.