Vision-based sign language recognition

Pao Yue-kong Library Electronic Theses Database

Vision-based sign language recognition

 

Author: Bao, Hanqing
Title: Vision-based sign language recognition
Degree: M.Sc.
Year: 2014
Subject: Optical pattern recognition.
Sign language.
Human locomotion -- Analysis -- Data processing.
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Computing
Pages: viii, 59 leaves : ill. ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2752266
URI: http://theses.lib.polyu.edu.hk/handle/200/7511
Abstract: Sign language recognition is an important subfield of Human Computer Interaction (HCI), which enables people communicating with hearing impaired people conveniently. Many different methods are proposed and apply to the recognition problem previously including several dimensionality reducing methods and classification methods. However due to the complexity of human signs, it has never been an easy task. In this dissertation, different hand shapes of human signers are tracked and segmented from dynamic image sequences with analysis of RGB data and depth data captured by a Kinect sensor. A sparse autoencoder is trained to reconstruct hand shape images into high-level features. The learned features were then fed into a multinomial logistic regression model and trained it to classify signs of digit from 0-9 of Chinese Sign Language (CSL). A total of 4000 hand shape images are collected and used during the experiments and produced reasonable results. The performance of entire system is good to achieve almost realtime sign recognition after the model being trained.

Files in this item

Files Size Format
b27522660.pdf 5.168Mb PDF
Copyright Undertaking
As a bona fide Library user, I declare that:
  1. I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
  2. I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
  3. I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.

     

Quick Search

Browse

More Information