| _version_ |
1860799667387760640
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| building |
INTELEK Repository
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| collection |
Online Access
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| collectionurl |
https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2018-08-02 10:20:49
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| eventvenue |
Istanbul, Turkey
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| format |
Restricted Document
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| id |
6918
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| institution |
UniSZA
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| originalfilename |
1667-01-FH03-FIK-18-14464.jpg
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| person |
norman
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| recordtype |
oai_dc
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| resourceurl |
https://intelek.unisza.edu.my/intelek/pages/view.php?ref=6918
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| spelling |
6918 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=6918 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Conference Conference Paper image/jpeg inches 96 96 norman 51 51 753 2018-08-02 10:20:49 1406x753 1406 1667-01-FH03-FIK-18-14464.jpg UniSZA Private Access Age prediction on face features via multiple classifiers Active Appearance Models (AAM), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Support Vector Regression (SVR) Human age recognition becomes increasingly important due to its beneficial employments alongside security and computer applications. Age prediction from face picture has a lot of challenges, such as insufficiency of training data and uncontrollable situation. In this research, we address these critical issues by introducing an improved age prediction algorithm using Active Appearance Models (AAM) and three classifiers, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Support Vector Regression (SVR) to improve the precision of age prediction based on the present methods. In this algorithm, the traits of the facial pictures are explicated as traits vectors by AAM model, and the classifiers are utilized to estimate the age. We were able to recognize that the accuracy of SVR algorithm is better than the accuracy of KNN and SVM classifiers. 4th International Conference on Computer and Technology Applications, ICCTA 2018 Istanbul, Turkey
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| spellingShingle |
Age prediction on face features via multiple classifiers
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| summary |
Human age recognition becomes increasingly important due to its beneficial employments alongside security and computer applications. Age prediction from face picture has a lot of challenges, such as insufficiency of training data and uncontrollable situation. In this research, we address these critical issues by introducing an improved age prediction algorithm using Active Appearance Models (AAM) and three classifiers, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Support Vector Regression (SVR) to improve the precision of age prediction based on the present methods. In this algorithm, the traits of the facial pictures are explicated as traits vectors by AAM model, and the classifiers are utilized to estimate the age. We were able to recognize that the accuracy of SVR algorithm is better than the accuracy of KNN and SVM classifiers.
|
| title |
Age prediction on face features via multiple classifiers
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| title_full |
Age prediction on face features via multiple classifiers
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| title_fullStr |
Age prediction on face features via multiple classifiers
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| title_full_unstemmed |
Age prediction on face features via multiple classifiers
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| title_short |
Age prediction on face features via multiple classifiers
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| title_sort |
age prediction on face features via multiple classifiers
|