The Integrated Usage of Lbp and Hog Transformations and Machine Learning Algorithms for Age Range Prediction From Facial Images

dc.authorid Şengül, Gökhan/0000-0003-2273-4411
dc.authorscopusid 57204921319
dc.authorscopusid 8402817900
dc.authorwosid Sengul, Gokhan/G-8213-2016
dc.authorwosid Şengül, Gökhan/AAA-2788-2022
dc.contributor.author Khalifa, Tariq
dc.contributor.author Sengul, Gokhan
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-07-05T15:27:27Z
dc.date.available 2024-07-05T15:27:27Z
dc.date.issued 2018
dc.department Atılım University en_US
dc.department-temp [Khalifa, Tariq] Al Jabal Al Gharbi Univ, Gharian 64200, Libya; [Sengul, Gokhan] Atilim Univ, TR-06836 Incek Ankara, Turkey en_US
dc.description Şengül, Gökhan/0000-0003-2273-4411 en_US
dc.description.abstract Age prediction is an active study field that can be used in many computer vision problems due to its importance and effectiveness. In this paper, we present extensive experiments and provide an efficient and accurate approach for age range prediction of people from facial images. First, we apply image resizing to unify all images' size, and Histogram Equalization technique to reduce the illumination effects on all facial images taken from FG-NET and UTD aging databases. Second, Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are used to extract the features of these images, and then we combined both HOG and LBP features in order to attain better prediction. Finally, Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) are used for the classification processes. In addition, k-fold, Leave-One-Out (LOO) and Confusion Matrix (CM) are used to evaluate the performance of proposed methods. The extensive and intensified experiments show that combining HOG and LBP features improved the age range predicting performance up to 99.87%. en_US
dc.identifier.citationcount 1
dc.identifier.doi 10.17559/TV-20170308030459
dc.identifier.endpage 1362 en_US
dc.identifier.issn 1330-3651
dc.identifier.issn 1848-6339
dc.identifier.issue 5 en_US
dc.identifier.scopus 2-s2.0-85057778233
dc.identifier.scopusquality Q3
dc.identifier.startpage 1356 en_US
dc.identifier.uri https://doi.org/10.17559/TV-20170308030459
dc.identifier.uri https://hdl.handle.net/20.500.14411/2657
dc.identifier.volume 25 en_US
dc.identifier.wos WOS:000448434200013
dc.identifier.wosquality Q4
dc.institutionauthor Şengül, Gökhan
dc.language.iso en en_US
dc.publisher Univ Osijek, Tech Fac en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 6
dc.subject age prediction en_US
dc.subject facial images en_US
dc.subject Histogram of Oriented Gradient (HOG) en_US
dc.subject kNN en_US
dc.subject Local Binary Pattern (LBP) en_US
dc.subject SVM en_US
dc.title The Integrated Usage of Lbp and Hog Transformations and Machine Learning Algorithms for Age Range Prediction From Facial Images en_US
dc.type Article en_US
dc.wos.citedbyCount 3
dspace.entity.type Publication
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