Finger spelling recognition using neural network

Lim, Kian Ming and Tan, Kok Sean and Tan, Alan Wee Chiat and Tan, Shing Chiang and Lee, Chin Poo and Abdul Razak, Siti Fatimah (2016) Finger spelling recognition using neural network. In: 2015 IEEE Student Conference on Research and Development (SCOReD). IEEE Xplore, pp. 78-81. ISBN 978-1-4673-9572-4

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Official URL: http://doi.org/10.1109/SCORED.2015.7449437

Abstract

Finger spelling is a way of communication by expressing words using hand signs in order to ensure deaf and dumb community can communicate with others effectively. Therefore, a system that can understand finger spelling is needed. As a result of that, this work is conducted to primarily develop a tutoring system for finger spelling. To develop a robust real-time finger spelling tutoring system, it is necessary to ensure the accuracy of the finger spelling recognition. Even though there are existing solutions available for a decade, but most of them are just focusing on improving accuracy rate without implementing their solutions as a complete tutoring system for finger spelling. Consequently, it inspires this research project to develop a tutoring system for finger spelling. Microsoft Kinect sensor is used to acquire color images and depth images of the finger spells. Depth images are used to perform segmentation on the color images. After that, the segmented images are used as input and pass into a two hidden layers backpropagation neural network for classification.

Item Type: Book Section
Uncontrolled Keywords: backpropagation neural network, finger spelling recognition, Miscrosoft Kinect sensor
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering and Technology (FET)
Faculty of Information Science and Technology (FIST)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 16 Feb 2017 05:04
Last Modified: 16 Feb 2017 05:04
URI: http://shdl.mmu.edu.my/id/eprint/6439

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