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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 644266260
doi: 10.4316/AECE


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  1/2014 - 11

Face Recognition using Similarity Pattern of Image Directional Edge Response

BASHAR, F. See more information about BASHAR, F. on SCOPUS See more information about BASHAR, F. on IEEExplore See more information about BASHAR, F. on Web of Science, KHAN, A. See more information about  KHAN, A. on SCOPUS See more information about  KHAN, A. on SCOPUS See more information about KHAN, A. on Web of Science, AHMED, F. See more information about  AHMED, F. on SCOPUS See more information about  AHMED, F. on SCOPUS See more information about AHMED, F. on Web of Science, KABIR, H. See more information about KABIR, H. on SCOPUS See more information about KABIR, H. on SCOPUS See more information about KABIR, H. on Web of Science
 
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Download PDF pdficon (907 KB) | Citation | Downloads: 518 | Views: 1,829

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
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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.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 13,249 TCR
SCOPUS® Citations for all references: 17,195 TCR

Web of Science® Average Citations per reference: 379 ACR
SCOPUS® Average Citations per reference: 491 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2016-12-07 07:18 in 97 seconds.




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