Abstract—Human gesture is a form of body language usually used as a mean of communication and is very critical in human-robot interactions. Vision-based gesture recognition methods to detect hand motion are vital to support such interactions. Hand gesture recognition enables a convenient and usable interface between devices and users. In this paper, an approach is presented for hand gesture recognition based on image processing methods, namely Wavelets Transform (WT), Empirical Mode Decomposition (EMD), besides Artificial Intelligence classifier which is Artificial Neural Networks (ANN) and Convolutional Neural Network (CNN). The methods are evaluated based on many factors including execution time, accuracy, sensitivity, specificity, positive and negative predictive value, likelihood, receiver operating characteristic (ROC), area under roc curve (AUC) and root mean Square. Results indicate that WT have less execution time than EMD and CNN. In addition, CNN is more effective in extracting distinct features and classifying data accurately compared to EMD and WT.
Index Terms—Artificial neural networks, convolutional neural network, empirical mode decomposition, hand gesture recognition, wavelet transform.
The authors are with the Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UK, UB8 3PH (e-mail: Norah.alnaim@brunel.ac.uk, Maysam.Abbod@brunel.ac.uk).
Cite: N. Alnaim and M. Abbod, "Hand Gesture Detection Using Neural Networks Algorithms," International Journal of Machine Learning and Computing vol. 9, no. 6, pp. 782-787, 2019.
Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).