Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities

Hira Ansar, Ahmad Jalal, Munkhjargal Gochoo and Kibum Kim
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Hira Ansar: Department of Computer Science, Air University, Islamabad 44000, Pakistan
Ahmad Jalal: Department of Computer Science, Air University, Islamabad 44000, Pakistan
Munkhjargal Gochoo: Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain 15551, United Arab Emirates
Kibum Kim: Department of Human-Computer Interaction, Hanyang University, Ansan 15588, Korea

Sustainability, 2021, vol. 13, issue 5, 1-26

Abstract: Due to the constantly increasing demand for the automatic localization of landmarks in hand gesture recognition, there is a need for a more sustainable, intelligent, and reliable system for hand gesture recognition. The main purpose of this study was to develop an accurate hand gesture recognition system that is capable of error-free auto-landmark localization of any gesture dateable in an RGB image. In this paper, we propose a system based on landmark extraction from RGB images regardless of the environment. The extraction of gestures is performed via two methods, namely, fused and directional image methods. The fused method produced greater extracted gesture recognition accuracy. In the proposed system, hand gesture recognition (HGR) is done via several different methods, namely, (1) HGR via point-based features, which consist of (i) distance features, (ii) angular features, and (iii) geometric features; (2) HGR via full hand features, which are composed of (i) SONG mesh geometry and (ii) active model. To optimize these features, we applied gray wolf optimization. After optimization, a reweighted genetic algorithm was used for classification and gesture recognition. Experimentation was performed on five challenging datasets: Sign Word, Dexter1, Dexter + Object, STB, and NYU. Experimental results proved that auto landmark localization with the proposed feature extraction technique is an efficient approach towards developing a robust HGR system. The classification results of the reweighted genetic algorithm were compared with Artificial Neural Network (ANN) and decision tree. The developed system plays a significant role in healthcare muscle exercise.

Keywords: directional image; geodesic distance; gray wolf optimization; hand gesture recognition; landmark localization; reweighted genetic algorithm; saliency map (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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