• Mar 27, 2019 News!Good News! All papers from Volume 9, Number 1 have been indexed by Scopus!   [Click]
  • May 07, 2019 News!Vol.9, No.3 has been published with online version.   [Click]
  • Mar 30, 2019 News!Vol.9, No.2 has been published with online version.   [Click]
Search
General Information
    • ISSN: 2010-3700
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus(since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2019 Vol.9(2): 135-142 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.2.777

Robustness Analysis of 3D Convolutional Neural Network for Human Hand Gesture Recognition

Dang-Manh Truong, Huong-Giang Doan, Thanh-Hai Tran, Hai Vu, and Thi-Lan Le
Abstract—Recently, a number of methods for dynamic hand gesture recognition has been proposed. However, deployment of such methods in a practical application still has to face with many challenges due to the variation of view point, complex background or subject style. In this work, we deeply investigate performance of advanced convolutional neural networks for a specific case of hand gestures and evaluate how robust it is to above variations. To this end, we adopt an existing 3D convolutional neural network which was originally proposed for general human action recognition and obtained very competitive accuracy. We extend it to two-streams architecture (RGB and optical flow) and apply transfer learning on our dataset of hand gestures. To evaluate the robustness of the method, we design carefully a multi-view dataset that composes of five dynamic hand gestures in indoor environment with complex background. Experiments with single or cross view on this dataset show that background and viewpoint has strong impact on recognition robustness. In addition, the network’s performances are mostly increased by multi-modality combinations and fine-tuning strategy. This analysis helps to make

Index Terms—Deep learning, convolutional neural network, dynamic hand gestures, optical flow, multi-view.

Dang-Manh Truong was with Hanoi University of Science Technology, Vietnam (e-mail: dangmanhtruong@gmail.com).
Huong-Giang Doan is with Electrical Power University Hanoi, Vietnam (e-mail: giangdth@epu.edu.vn).
Thanh-Hai Tran, Hai Vu, and Thi-Lan Le are with Hanoi University of Science Technology (Corresponding author: Thanh-Hai Tran; e-mail: thanh-hai.tran@mica.edu.vn, hai.vu@mica.edu.vn, Thi-Lan.Le@mica.edu.vn).

[PDF]

Cite: Dang-Manh Truong, Huong-Giang Doan, Thanh-Hai Tran, Hai Vu, and Thi-Lan Le, "Robustness Analysis of 3D Convolutional Neural Network for Human Hand Gesture Recognition," International Journal of Machine Learning and Computing vol. 9, no. 2, pp. 135-142, 2019.

Copyright © 2008-2019. International Journal of Machine Learning and Computing. All rights reserved.
E-mail: ijmlc@ejournal.net