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JCR Impact Factor: 0.825
JCR 5-Year IF: 0.752
SCOPUS CiteScore: 2.5
Issues per year: 4
Current issue: May 2023
Next issue: Aug 2023
Avg review time: 75 days
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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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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.

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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.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

2021-Jun-06
SCOPUS published the CiteScore for 2020, computed by using an improved methodology, counting the citations received in 2017-2020 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 2020 is 2.5, better than all our previous results.

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  2/2015 - 12

 HIGH-IMPACT PAPER 

Towards Real-Life Facial Expression Recognition Systems

BENTA, K.-I. See more information about BENTA, K.-I. on SCOPUS See more information about BENTA, K.-I. on IEEExplore See more information about BENTA, K.-I. on Web of Science, VAIDA, M.-F. See more information about VAIDA, M.-F. on SCOPUS See more information about VAIDA, M.-F. on SCOPUS See more information about VAIDA, M.-F. on Web of Science
 
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Download PDF pdficon (1,034 KB) | Citation | Downloads: 1,053 | Views: 5,913

Author keywords
facial expression recognition, affective computing, feature extraction, classification, database

References keywords
recognition(74), facial(70), computing(20), pattern(19), emotion(16), analysis(15), automatic(14), affective(14), image(12), vision(10)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-05-31
Volume 15, Issue 2, Year 2015, On page(s): 93 - 102
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.02012
Web of Science Accession Number: 000356808900012
SCOPUS ID: 84979726417

Abstract
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Facial expressions are a set of symbols of great importance for human-to-human communication. Spontaneous in their nature, diverse and personal, facial expressions demand for real-time, complex, robust and adaptable facial expression recognition (FER) systems to facilitate the human-computer interaction. The last years' research efforts in the recognition of facial expressions are preparing FER systems to step into the real-life. In order to meet the before-mentioned requirements, this article surveys the work in FER since 2008, particularly adopting the discrete states emotion model in a quest for the most valuable FER works/systems. We first present the new spontaneous facial expression databases and then organize the real-time FER solutions grouped by spontaneous and posed facial expression databases. Then automatic FERs are compared and the cross-database validation method is presented. Finally, we outline FER system open issues to meet real-life challenges.


References | Cited By

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 12 [View]
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Cited-By SCOPUS

SCOPUS® Times Cited: 13
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Cited-By CrossRef

[1] Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi, Rathour, Navjot, Khanam, Zeba, Gehlot, Anita, Singh, Rajesh, Rashid, Mamoon, AlGhamdi, Ahmed Saeed, Alshamrani, Sultan S., Applied Sciences, ISSN 2076-3417, Issue 22, Volume 11, 2021.
Digital Object Identifier: 10.3390/app112210540
[CrossRef]

[2] Anatomization of the systems of dimension relaxation for facial recognition, Raha, Mayamin Hamid, Deb, Tonmoay, Rahmun, Mahieyin, Chen, Tim, Intelligent Decision Technologies, ISSN 1872-4981, Issue 4, Volume 14, 2021.
Digital Object Identifier: 10.3233/IDT-190120
[CrossRef]

[3] Mining Inconsistent Emotion Recognition Results With the Multidimensional Model, Landowska, Agnieszka, Zawadzka, Teresa, Zawadzki, Michal, IEEE Access, ISSN 2169-3536, Issue , 2022.
Digital Object Identifier: 10.1109/ACCESS.2021.3139078
[CrossRef]

[4] Software Architecture Design for Spatially-Indexed Media in Smart Environments, SCHIPOR, O.-A., WU, W., TSAI, W.-T., VATAVU, R.-D., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 2, Volume 17, 2017.
Digital Object Identifier: 10.4316/AECE.2017.02003
[CrossRef] [Full text]

[5] The current challenges of automatic recognition of facial expressions: A systematic review, Masson, Audrey, Cazenave, Guillaume, Trombini, Julien, Batt, Martine, AI Communications, ISSN 1875-8452, Issue 3-6, Volume 33, 2020.
Digital Object Identifier: 10.3233/AIC-200631
[CrossRef]

[6] SenseGraph: Affect Self-monitoring and Tagging Tool with Wearable Devices, Alpers, Sebastian, Benta, Kuderna-Iulian, 2021 International Conference on Computer Communications and Networks (ICCCN), ISBN 978-1-6654-1278-0, 2021.
Digital Object Identifier: 10.1109/ICCCN52240.2021.9522286
[CrossRef]

[7] The affinity platform, Ardelean, Alexandru, BenĊ£a, Kuderna-Iulian, Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers, ISBN 9781450380768, 2020.
Digital Object Identifier: 10.1145/3410530.3414371
[CrossRef]

[8] Comparison of selected off-the-shelf solutions for emotion recognition based on facial expressions, Brodny, Grzegorz, Kolakowska, Agata, Landowska, Agnieszka, Szwoch, Mariusz, Szwoch, Wioleta, Wrobel, Michal R., 2016 9th International Conference on Human System Interactions (HSI), ISBN 978-1-5090-1729-4, 2016.
Digital Object Identifier: 10.1109/HSI.2016.7529664
[CrossRef]

[9] A multimodal affective monitoring tool for mobile learning, Benta, Kuderna-Iulian, Cremene, Marcel, Vaida, Mircea-Florin, 2015 14th RoEduNet International Conference - Networking in Education and Research (RoEduNet NER), ISBN 978-1-4673-8179-6, 2015.
Digital Object Identifier: 10.1109/RoEduNet.2015.7311824
[CrossRef]

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


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