a face recognition approach using zernike moments for video surveillance
Wiliem, Arnold, Madasu, Vamsi K., Boles, Wageeh W., & Yarlagadda, Prasad K. (2007) a face recognition approach using zernike moments for video surveillance. In Mendis, Priyan, Lai, Joseph, Dawson, Ed, & Abbass, Hussein (Eds.) RNSA Security Technology Conference, 28 Sept 2007, Melbourne, Australia.
In this paper, a face recognition approach using Zernike moments is presented for the main purpose of detecting faces in surveillance cameras. Zernike moments are
invariant to rotation and scale and these properties make them an appropriate feature for automatic face recognition. A Viola-Jones detector based on the Adaboost algorithm is employed for detecting the face within an image sequence. Preprocessing is carried out wherever it is needed. A fuzzy enhancement algorithm is also applied to achieve uniform illumination. Zernike moments are then computed from each detected facial image. The final classification is achieved using a kNN classifier. The performance of the proposed methodology is compared on three different benchmark datasets. The results illustrate the efficacy of Zernike moments for the face recognition problem in video surveillance.
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Conference Paper|
|Additional Information:||The contents of this journal can be freely accessed online via the journal’s web page (see hypertext link).|
|Keywords:||face recognition, surveillance, zernike, zernike moments|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)|
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
|Copyright Owner:||All rights reserved. Other than brief extracts, no part of this publication may be produced in any form without the written consent of the publisher. The Publisher makes no representation or warranty regarding the accuracy, timeliness, suitability or any other aspect of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made|
|Copyright Statement:||Copyright 2007 Australian Homeland Security Research Centre|
|Deposited On:||03 Oct 2007|
|Last Modified:||01 Apr 2009 12:19|
Repository Staff Only: item control page