Automatic estimation of taste liking through facial expression dynamics

Simple item page
buir.contributor.authorDibeklioglu, Hamdi
dc.citation.epage163en_US
dc.citation.issueNumber1en_US
dc.citation.spage151en_US
dc.citation.volumeNumber11en_US
dc.contributor.authorDibeklioglu, Hamdien_US
dc.contributor.authorGevers, T.en_US
dc.date.accessioned2021-02-18T08:40:41Z
dc.date.available2021-02-18T08:40:41Z
dc.date.issued2020
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
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 2,970 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 percent 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.en_US
dc.description.provenanceSubmitted by Onur Emek (onur.emek@bilkent.edu.tr) on 2021-02-18T08:40:41Z No. of bitstreams: 1 Automatic_Estimation_of_Taste_Liking_Through_Facial_Expression_Dynamics.pdf: 2154991 bytes, checksum: 51cbe69e1b59f8aaf2107839ebbba2c8 (MD5)en
dc.description.provenanceMade available in DSpace on 2021-02-18T08:40:41Z (GMT). No. of bitstreams: 1 Automatic_Estimation_of_Taste_Liking_Through_Facial_Expression_Dynamics.pdf: 2154991 bytes, checksum: 51cbe69e1b59f8aaf2107839ebbba2c8 (MD5) Previous issue date: 2020en
dc.identifier.doi10.1109/TAFFC.2018.2832044en_US
dc.identifier.issn1949-3045
dc.identifier.urihttp://hdl.handle.net/11693/75434
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttps://dx.doi.org/10.1109/TAFFC.2018.2832044en_US
dc.source.titleIEEE Transactions on Affective Computingen_US
dc.subjectTaste likingen_US
dc.subjectTaste appreciationen_US
dc.subjectFacial expression dynamicsen_US
dc.subjectSpontaneous expressionen_US
dc.subjectTaste-induced expressionen_US
dc.titleAutomatic estimation of taste liking through facial expression dynamicsen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Automatic_Estimation_of_Taste_Liking_Through_Facial_Expression_Dynamics.pdf
Size:
2.06 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Scholarly Publications - Computer Engineering