NDLI: Classification of Skeletal Wireframe Representation of Hand Gesture Using Complex-Valued Neural Network

Content Provider	Springer Nature Link
Author	Hafiz, Abdul Rahman Al Nuaimi, Ahmed Yarub Amin, Md. Faijul Murase, Kazuyuki
Copyright Year	2014
Abstract	Complex-valued neural networks (CVNNs), that allow processing complex-valued data directly, have been applied to a number of practical applications, especially in signal and image processing. In this paper, we apply CVNN as a classification algorithm for the skeletal wireframe data that are generated from hand gestures. A CVNN having one hidden layer that maps complex-valued input to real-valued output was used, a training algorithm based on Levenberg Marquardt algorithm (CLMA) was derived, and a task to recognize 26 different gestures that represent English alphabet was given. The initial image processing part consists of three modules: real-time hand tracking, hand-skeletal construction, and hand gesture recognition. We have achieved; (1) efficient and accurate gesture extraction and representation in complex domain, (2) training of the CVNN utilising CLMA, and (3) providing a proof of the superiority of the aforementioned methods by utilising complex-valued learning vector quantization. A comparison with real-valued neural network shows that a CVNN with CLMA provides higher recognition performance, accompanied by significantly faster training. Moreover, a comparison of six different activation functions was performed and their utility is argued.
Starting Page	649
Ending Page	664
Page Count	16
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	42
Issue Number	3
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2014-08-17
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Skeletal wireframe representation Complex-valued neural network Levenberg Marquardt algorithm Learning vector quantization Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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