Article
Version 1
Preserved in Portico This version is not peer-reviewed
Visual Static Hand Gesture Recognition Using CNN
Version 1
: Received: 8 June 2023 / Approved: 9 June 2023 / Online: 9 June 2023 (04:00:04 CEST)
A peer-reviewed article of this Preprint also exists.
Eid, A.; Schwenker, F. Visual Static Hand Gesture Recognition Using Convolutional Neural Network. Algorithms 2023, 16, 361. Eid, A.; Schwenker, F. Visual Static Hand Gesture Recognition Using Convolutional Neural Network. Algorithms 2023, 16, 361.
Abstract
Hand gestures are an essential part of human-to-human communication and interaction, and therefore for technical applications, the aim is increasingly to achieve interaction between humans and computers that is as natural as possible, for example by means of natural language or hand gestures. In the context of human-machine interaction research, these methods are consequently being explored more and more. However, the realization of natural communication between humans and computers is a major challenge. In the field of hand gesture recognition, research approaches are being pursued that use additional hardware such as special gloves to classify gestures with high accuracy. Recently, deep learning techniques using artificial neural networks have been increasingly proposed for the problem of gesture recognition without using such tools. In this context, in our approach convolutional neural network (CNN) will be explored in detail for the task of hand gesture recognition. CNN is a deep neural network that can be used in the field of visual object processing and classification. The goal of this work is to recognize ten types of static hand gestures in front of complex backgrounds and different hand sizes based on raw images without the use of extra hardware. We achieved good results with a CNN network architecture consisting of seven layers. Through data augmentation and skin segmentation, a significant increase of the model accuracy was achieved. On public benchmarks the ten gestures have been classified almost perfectly with a testing accuracy of 96.5%
Keywords
Static gesture recognition; CNN; Color model transform; Skin color segmentation; Preprocessing; Data augmentation; Adam optimizer; Cross-Entropy loss
Subject
Computer Science and Mathematics, Computer Science
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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