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dc.contributor.authorBarros, Pablo
dc.contributor.authorChuramani, Nikhil
dc.contributor.authorSciutti, Alessandra
dc.date.accessioned2020-10-07T16:18:27Z
dc.date.available2020-10-07T16:18:27Z
dc.date.issued2020-10-06
dc.date.submitted2020-06-18
dc.identifier.issn2662-995X
dc.identifier.others42979-020-00325-6
dc.identifier.other325
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/311181
dc.descriptionFunder: Istituto Italiano di Tecnologia
dc.description.abstractAbstract: Current state-of-the-art models for automatic facial expression recognition (FER) are based on very deep neural networks that are effective but rather expensive to train. Given the dynamic conditions of FER, this characteristic hinders such models of been used as a general affect recognition. In this paper, we address this problem by formalizing the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks. We introduce an inhibitory layer that helps to shape the learning of facial features in the last layer of the network and, thus, improving performance while reducing the number of trainable parameters. To evaluate our model, we perform a series of experiments on different benchmark datasets and demonstrate how the FaceChannel achieves a comparable, if not better, performance to the current state-of-the-art in FER. Our experiments include cross-dataset analysis, to estimate how our model behaves on different affective recognition conditions. We conclude our paper with an analysis of how FaceChannel learns and adapts the learned facial features towards the different datasets.
dc.languageen
dc.publisherSpringer Singapore
dc.subjectOriginal Research
dc.subjectFacial expression recognition
dc.subjectConvolutional neural network
dc.subjectAffective computing
dc.titleThe FaceChannel: A Fast and Furious Deep Neural Network for Facial Expression Recognition
dc.typeArticle
dc.date.updated2020-10-07T16:18:26Z
prism.issueIdentifier6
prism.publicationNameSN Computer Science
prism.volume1
dc.identifier.doi10.17863/CAM.58273
dcterms.dateAccepted2020-09-08
rioxxterms.versionofrecord10.1007/s42979-020-00325-6
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidBarros, Pablo [0000-0002-6517-682X]
dc.identifier.eissn2661-8907
pubs.funder-project-idEuropean Research CouncilEuropean Research Council (804388)


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