Open Access

ARTICLE

Hand Gesture Recognition for Disabled People Using Bayesian Optimization with Transfer Learning

by Fadwa Alrowais¹, Radwa Marzouk^2,3, Fahd N. Al-Wesabi^4,*, Anwer Mustafa Hilal⁵

1 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Mathematics, Faculty of Science, Cairo University, Giza, 12613, Egypt
4 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
5 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Fahd N. Al-Wesabi. Email:

Intelligent Automation & Soft Computing 2023, 36(3), 3325-3342. https://doi.org/10.32604/iasc.2023.036354

Received 27 September 2022; Accepted 14 November 2022; Issue published 15 March 2023

Abstract

Sign language recognition can be treated as one of the efficient solutions for disabled people to communicate with others. It helps them to convey the required data by the use of sign language with no issues. The latest developments in computer vision and image processing techniques can be accurately utilized for the sign recognition process by disabled people. American Sign Language (ASL) detection was challenging because of the enhancing intraclass similarity and higher complexity. This article develops a new Bayesian Optimization with Deep Learning-Driven Hand Gesture Recognition Based Sign Language Communication (BODL-HGRSLC) for Disabled People. The BODL-HGRSLC technique aims to recognize the hand gestures for disabled people’s communication. The presented BODL-HGRSLC technique integrates the concepts of computer vision (CV) and DL models. In the presented BODL-HGRSLC technique, a deep convolutional neural network-based residual network (ResNet) model is applied for feature extraction. Besides, the presented BODL-HGRSLC model uses Bayesian optimization for the hyperparameter tuning process. At last, a bidirectional gated recurrent unit (BiGRU) model is exploited for the HGR procedure. A wide range of experiments was conducted to demonstrate the enhanced performance of the presented BODL-HGRSLC model. The comprehensive comparison study reported the improvements of the BODL-HGRSLC model over other DL models with maximum accuracy of 99.75%.

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Keywords

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