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Face Recognition using Similarity Pattern of Image Directional Edge ResponseBASHAR, F.   , KHAN, A. , AHMED, F. , KABIR, H. |
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Author keywords
discrete cosine transform, face recognition, feature extraction, image texture analysis, pattern analysis
References keywords
recognition(31), face(25), pattern(17), local(11), analysis(10), image(8), binary(6), vision(5), machine(5), information(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2014-02-28
Volume 14, Issue 1, Year 2014, On page(s): 69 - 76
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.01011
Web of Science Accession Number: 000332062300011
SCOPUS ID: 84894635007
Abstract
An effective face descriptor is critical for a successful face recognition system and must overcome the challenges of changing environment. The face representation must have discriminatory information and be computationally feasible for any face recognition system. In this paper we propose a new face descriptor, Similarity Pattern of Image Directional Edge Response (SPIDER), for face recognition. An image is divided into smaller local regions and 8 directional edge responses are generated for each pixel position in the regions. The regional cumulative response of each direction is calculated and a histogram is generated consisting of 8 bins, one for each of the directions. The SPIDER code is generated by calculating the similarity between the histogram of the local region around each pixel against the histogram of neighbor regions. The feature vector is projected to a low-dimension vector space using a dimension reduction method to minimize the classification time. Experiments using the proposed method were carried out on the FERET database and results show improved recognition rates indicating the robustness to changing environment, and a low classification time compared to the existing methods. |
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[5] Face Recognition Performance Improvement using a Similarity Score of Feature Vectors based on Probabilistic Histograms, SRIKOTE, G., MEESOMBOON, A., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 16, 2016.
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[8] Face Recognition Using Gradient Texture Features, Narasimha Reddy, Soora, Modika, Ishwarya, Tejashwini, Gummadi, Mythri, Tanneru, Karthik Kumar, Kadarla Rohan, Raj, Ubbani Samson, 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), ISBN 978-1-6654-7413-9, 2022.
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Stefan cel Mare University of Suceava, Romania
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
