Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/20861
Title: Recognition of Holoscopic 3D Video Hand Gesture Using Convolutional Neural Networks
Authors: Alnaim, N
Abbod, M
Swash, R
Keywords: computer vision;gesture recognition;hand gesture;3D hand gesture recognition;artificial intelligence;machine learning;deep learning;convolutional neural network
Issue Date: 15-Apr-2020
Publisher: MDPI AG
Citation: Technologies, 8 (2), pp. 19 - 19
Abstract: The convolutional neural network (CNN) algorithm is one of the efficient techniques to recognize hand gestures. In human–computer interaction, a human gesture is a non-verbal communication mode, as users communicate with a computer via input devices. In this article, 3D micro hand gesture recognition disparity experiments are proposed using CNN. This study includes twelve 3D micro hand motions recorded for three different subjects. The system is validated by an experiment that is implemented on twenty different subjects of different ages. The results are analysed and evaluated based on execution time, training, testing, sensitivity, specificity, positive and negative predictive value, and likelihood. The CNN training results show an accuracy as high as 100%, which present superior performance in all factors. On the other hand, the validation results average about 99% accuracy. The CNN algorithm has proven to be the most accurate classification tool for micro gesture recognition.
URI: http://bura.brunel.ac.uk/handle/2438/20861
DOI: http://dx.doi.org/10.3390/technologies8020019
ISSN: 2227-7080
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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