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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: 423 | Views: 2,605

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), zhang(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.


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References Weight

Web of Science® Citations for all references: 4,675 TCR
SCOPUS® Citations for all references: 7,235 TCR

Web of Science® Average Citations per reference: 50 ACR
SCOPUS® Average Citations per reference: 77 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 2017-10-17 12:28 in 722 seconds.




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