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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1284
DC FieldValueLanguage
dc.contributor.authorPham Quoc Trungen_US
dc.date.accessioned2022-05-31T03:28:17Z-
dc.date.available2022-05-31T03:28:17Z-
dc.date.issued2021-
dc.identifier.urihttps://epub.vgu.edu.vn/handle/dlibvgu/1284-
dc.description.abstractAccording to many studies, face images can provide quite a lot of information about health status. The outburst of social networks and mobile devices allows researchers to access this information more accessible for many health care purposes. This project focuses on estimating Body mass index (BMI) based on facial images using the deep neural network because of its outstanding performance in many computer vision tasks. Specifically, we adopt the concept proposed by Dantcheva, Bremond and Bilinski, 2018 and deploy an efficient neural network, namely ResNet, as features extractor, followed by fully connected layers in charge of regression model for BMI estimation. After being trained on the VIP Attribute dataset, we evaluated this model for accuracy on two distinct datasets. The initial dataset randomly is chosen 20% from the VIP Attribute set, and the model’s mean absolute error (MAE) reached 2.8. For the dataset collected from Vietnamese participants, the MAE is 2.57. The positive result demonstrates the potential of creating a large-scale computer system for BMI prediction using facial picturesen_US
dc.language.isoenen_US
dc.publisherVietnamese-German University-
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectBMI predictionen_US
dc.subjectFacial imagesen_US
dc.subjectDeep neural networken_US
dc.titleBMI prediction from facial images using deep neural networken_US
dc.typeThesisen_US
item.languageiso639-1other-
item.fulltextWith Fulltext-
item.grantfulltextrestricted-
Appears in Collections:Computer Science (CS)
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