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dc.contributor.authorDibeklioglu, H.en_US
dc.contributor.authorGevers, T.en_US
dc.date.accessioned2019-02-21T16:05:17Z
dc.date.available2019-02-21T16:05:17Z
dc.date.issued2018en_US
dc.identifier.issn1949-3045
dc.identifier.urihttp://hdl.handle.net/11693/50243
dc.description.abstractThe level of taste liking is an important measure for a number of applications such as the prediction of long-term consumer acceptance for different food and beverage products. Based on the fact that facial expressions are spontaneous, instant and heterogeneous sources of information, this paper aims to automatically estimate the level of taste liking through facial expression videos. Instead of using handcrafted features, the proposed approach deep learns the regional expression dynamics, and encodes them to a Fisher vector for video representation. Regional Fisher vectors are then concatenated, and classified by linear SVM classifiers. The aim is to reveal the hidden patterns of taste-elicited responses by exploiting expression dynamics such as the speed and acceleration of facial movements. To this end, we have collected the first large-scale beverage tasting database in the literature. The database has 2970 videos of taste-induced facial expressions collected from 495 subjects. Our large-scale experiments on this database show that the proposed approach achieves an accuracy of 70.37% for distinguishing between three levels of taste-liking. Furthermore, we assess the human performance recruiting 45 participants, and show that humans are significantly less reliable for estimating taste appreciation from facial expressions in comparison to the proposed method. IEEE
dc.language.isoEnglish
dc.source.titleIEEE Transactions on Affective Computingen_US
dc.relation.isversionofhttps://doi.org/10.1109/TAFFC.2018.2832044
dc.subjectAccelerationen_US
dc.subjectDatabasesen_US
dc.subjectEstimationen_US
dc.subjectFaceen_US
dc.subjectFacial expression dynamicsen_US
dc.subjectGolden_US
dc.subjectShapeen_US
dc.subjectSpontaneous expressionen_US
dc.subjectTaste appreciationen_US
dc.subjectTaste likingen_US
dc.subjectTaste-induced expressionen_US
dc.subjectVideosen_US
dc.titleAutomatic estimation of taste liking through facial expression dynamicsen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage1en_US
dc.citation.epage12en_US
dc.citation.volumeNumber20en_US
dc.identifier.doi10.1109/TAFFC.2018.2832044
dc.publisherInstitute of Electrical and Electronics Engineers


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