Convolutional neural networks with balanced batches for facial expressions recognition

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dc.authoridCangelosi, Angelo/0000-0002-4709-2243|Battini Sonmez, Elena/0000-0003-0090-984X
dc.authorwosidCangelosi, Angelo/D-6784-2011
dc.authorwosidBattini Sonmez, Elena/AAZ-6358-2021
dc.contributor.authorSonmez, Elena Battini
dc.contributor.authorCangelosi, Angelo
dc.date.accessioned2024-07-18T20:47:37Z
dc.date.available2024-07-18T20:47:37Z
dc.date.issued2017
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description9th International Conference on Machine Vision (ICMV) -- NOV 18-20, 2016 -- Nice, FRANCEen_US
dc.description.abstractThis paper considers the issue of fully automatic emotion classification on 2D faces. In spite of the great effort done in recent years, traditional machine learning approaches based on hand-crafted feature extraction followed by the classification stage failed to develop a real-time automatic facial expression recognition system. The proposed architecture uses Convolutional Neural Networks (CNN), which are built as a collection of interconnected processing elements to simulate the brain of human beings. The basic idea of CNNs is to learn a hierarchical representation of the input data, which results in a better classification performance. In this work we present a block-based CNN algorithm, which uses noise, as data augmentation technique, and builds batches with a balanced number of samples per class. The proposed architecture is a very simple yet powerful CNN, which can yield state-of-the-art accuracy on the very competitive benchmark algorithm of the Extended Cohn Kanade database.en_US
dc.description.sponsorshipSPIEen_US
dc.identifier.doi10.1117/12.2268412
dc.identifier.isbn978-1-5106-1132-0
dc.identifier.issn0277-786X
dc.identifier.issn1996-756X
dc.identifier.scopus2-s2.0-85029955822en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1117/12.2268412
dc.identifier.urihttps://hdl.handle.net/11411/7869
dc.identifier.volume10341en_US
dc.identifier.wosWOS:000410664800018en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpie-Int Soc Optical Engineeringen_US
dc.relation.ispartofNinth International Conference on Machine Vision (Icmv 2016)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAffective Computingen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectFacial Expression Recognitionen_US
dc.titleConvolutional neural networks with balanced batches for facial expressions recognition
dc.typeConference Object

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