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

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  1/2019 - 7

Circular Derivative Local Binary Pattern Feature Description for Facial Expression Recognition

TCHANGOU TOUDJEU, I. See more information about TCHANGOU TOUDJEU, I. on SCOPUS See more information about TCHANGOU TOUDJEU, I. on IEEExplore See more information about TCHANGOU TOUDJEU, I. on Web of Science, TAPAMO, J.-R. See more information about TAPAMO, J.-R. on SCOPUS See more information about TAPAMO, J.-R. on SCOPUS See more information about TAPAMO, J.-R. on Web of Science
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Author keywords
affective computing, classification, face recognition, feature extraction, image texture analysis

References keywords
facial(19), recognition(18), local(13), binary(11), patterns(8), pattern(8), image(6), classification(6), icme(4), comput(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2019-02-28
Volume 19, Issue 1, Year 2019, On page(s): 51 - 56
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.01007
Web of Science Accession Number: 000459986900007
SCOPUS ID: 85064192591

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This paper presents a novel feature extraction technique called circular derivative local binary pattern (CD-LBP) for Facial Expression Recognition (FER). Motivated by uniform local binary patterns (uLBPs) which exhibits high discriminative potential at a reduced data dimension of the original LBP feature vector, we extract CD-LBP feature descriptors as a result of binary derivatives of the circular binary patterns formed by LBPs. Seven datasets consisting of CD-LBP feature vectors are derived from the Japanese female facial expression (JAFFE) database, fed individually in a K-nearest neighbor classifier and evaluated with respect to their respective recognition rate and feature vector size. The experimental results demonstrate the relevance of the proposed feature description especially when performance metrics such as recognition accuracy and running time are considered.

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

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[2] X. Feng, M. Pietikinen, A. Hadid, "Facial Expression Recognition with Local Binary Patterns and Linear Programming," Pattern Recognition and Image Analysis, vol. 15, no. 2, pp. 546-548, 2005.
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[CrossRef] [SCOPUS Times Cited 7]

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[CrossRef] [SCOPUS Times Cited 59]

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[21] M. J. Lyons, J. Budynek, S. Akamatsu, "Automatic Classification of Single Facial Images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 12, pp. 1,357-1,362, 1999.
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[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 6]

References Weight

Web of Science® Citations for all references: 14,319 TCR
SCOPUS® Citations for all references: 19,513 TCR

Web of Science® Average Citations per reference: 551 ACR
SCOPUS® Average Citations per reference: 751 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 2022-05-17 06:19 in 145 seconds.

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