Enhancement of Hand Gesture Recognition Using Convolutional Neural Networks Integrating a Combination of an Autoencoder Network and PCA

Abstract

Hand gestures offer people a convenient way to interact with computers, in addition to give them the ability to communicate without physical contact and at a distance, which is essential in today’s health conditions, especially during an epidemic infectious viruses such as the COVID-19 coronavirus. However, factors, such as the complexity of hand gesture patterns, differences in hand size and position, and other aspects, can affect the performance of hand gesture recognition and classification algorithms. Some deep learning approaches such as convolutional neural networks (CNN), capsule networks (CapsNets) and autoencoders have been proposed by researchers to improve the performance of image recognition systems in this particular field: While CNNs are arguably the most widely used networks for object detection and image classification, CapsNets and Autoencoder seem to resolve some of the limitations identified in the first approach. For this reason, in this work, a specific combination of these networks is proposed to effectively solve the ASL problem. The results obtained in this work show that the proposed group with a simple data augmentation process improves precision performance by 99.43%.