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JCR Impact Factor: 0.700
JCR 5-Year IF: 0.700
SCOPUS CiteScore: 1.8
Issues per year: 4
Current issue: Nov 2024
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PUBLISHER

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: 643243560
doi: 10.4316/AECE


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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  1/2019 - 7

 HIGHLY CITED PAPER 

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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Download PDF pdficon (1,929 KB) | Citation | Downloads: 1,425 | Views: 3,018

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

Abstract
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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

Cited-By Clarivate Web of Science

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Cited-By SCOPUS

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Cited-By CrossRef

[1] Developing a machine learning-based approach for predicting road surface friction using dash camera images—a City of Edmonton, Canada, case study, Xie, Qian, Kwon, Tae J., Canadian Journal of Civil Engineering, ISSN 0315-1468, Issue 10, Volume 50, 2023.
Digital Object Identifier: 10.1139/cjce-2023-0015
[CrossRef]

[2] Modified feature extraction techniques to enhance face and expression recognition, Shrestha, Kshitiz, Alsadoon, Abeer, Al-Naymat, Ghazi, Jerew, Oday D., Multimedia Tools and Applications, ISSN 1573-7721, 2024.
Digital Object Identifier: 10.1007/s11042-024-20157-3
[CrossRef]

[3] MRI Brain Classification Using the Quantum Entropy LBP and Deep-Learning-Based Features, Hasan, Ali M., Jalab, Hamid A., Ibrahim, Rabha W., Meziane, Farid, AL-Shamasneh, Ala’a R., Obaiys, Suzan J., Entropy, ISSN 1099-4300, Issue 9, Volume 22, 2020.
Digital Object Identifier: 10.3390/e22091033
[CrossRef]

[4] Texture Analysis on Digital Microscopic Leather Images For Species Identification, Varghese, Anjli, Jawahar, Malathy, Prince, A Amalin, Gandomi, Amir H, 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), ISBN 979-8-3503-2088-6, 2022.
Digital Object Identifier: 10.1109/ISCMI56532.2022.10068472
[CrossRef]

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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