Hand Gesture Recognition with Convolution Neural Networks

Hand gestures are the most common forms of communication and have great importance in our world. They can help in building safe and comfortable user interfaces for a multitude of applications. Various computer vision algorithms have employed color and depth camera for hand gesture recognition, but r...

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Bibliographic Details
Published in2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) pp. 295 - 298
Main Author Zhan, Felix
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2019
Subjects
Big Data
Conferences
Convolution
Convolution Neural Networks
Convolutional neural networks
deep learning
Gesture recognition
Hand Gesture Recognition
Social networking (online)
Training
Online AccessGet full text
DOI10.1109/IRI.2019.00054

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Summary:Hand gestures are the most common forms of communication and have great importance in our world. They can help in building safe and comfortable user interfaces for a multitude of applications. Various computer vision algorithms have employed color and depth camera for hand gesture recognition, but robust classification of gestures from different subjects is still challenging. I propose an algorithm for real-time hand gesture recognition using convolutional neural networks (CNNs). The proposed CNN achieves an average accuracy of 98.76% on the dataset comprising of 9 hand gestures and 500 images for each gesture.
DOI:10.1109/IRI.2019.00054