Robust Gesture Recognition and Classification for Visually Impaired Persons Using Growth Optimizer with Deep Stacked Autoencoder

Abstract

Visual impairment affects the major population of the world, and impaired vision people need assistance for their day-to-day activities. With the enormous growth and usage of new technologies, various devices were developed to help them with object identification in addition to navigation in the indoor and outdoor surroundings. Gesture detection and classification for blind people aims to develop technologies to assist those people to navigate their surroundings more easily. To achieve this goal, using machine learning and computer vision techniques is a better solution to classify and detect hand gestures. Such methods are utilized for finding the shape, position, and movement of the hands in real-time. With this motivation, this article presents a robust gesture recognition and classification using growth optimizer with deep stacked autoencoder (RGRC-GODSAE) model for visually impaired persons. The goal of the RGRC-GODSAE technique lies in the accurate recognition and classification of gestures to assist visually impaired persons. The RGRC-GODSAE technique follows the Gabor filter approach at the initial stage to remove noise. In addition, the RGRC-GODSAE technique uses the ShuffleNet model as a feature extractor and the GO algorithm as a hyperparameter optimizer. Finally, the deep stacked autoencoder model is exploited for the automated recognition and classification of gestures. The experimental validation of the RGRC-GODSAE technique is carried out on the benchmark dataset. The extensive comparison study showed better gesture recognition performance of the RGRC-GODSAE technique over other deep learning models.