Age prediction on face features via multiple classifiers
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 criti...
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| 言語: | English |
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2018
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my-unisza-ir.16672020-11-19T06:24:03Z http://eprints.unisza.edu.my/1667/ Age prediction on face features via multiple classifiers Mohamad, F.S. Iqtait, M. Alsuhimat, F. QA Mathematics QA75 Electronic computers. Computer science 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. 2018 Conference or Workshop Item NonPeerReviewed image en http://eprints.unisza.edu.my/1667/1/FH03-FIK-18-14464.jpg Mohamad, F.S. and Iqtait, M. and Alsuhimat, F. (2018) Age prediction on face features via multiple classifiers. In: 4th International Conference on Computer and Technology Applications, ICCTA 2018, 03-05 May 2018, Istanbul, Turkey. |
| institution |
Universiti Sultan Zainal Abidin |
| building |
UNISZA Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Sultan Zainal Abidin |
| content_source |
UNISZA Institutional Repository |
| url_provider |
https://eprints.unisza.edu.my/ |
| language |
English |
| topic |
QA Mathematics QA75 Electronic computers. Computer science |
| spellingShingle |
QA Mathematics QA75 Electronic computers. Computer science Mohamad, F.S. Iqtait, M. Alsuhimat, F. Age prediction on face features via multiple classifiers |
| description |
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. |
| format |
Conference or Workshop Item |
| author |
Mohamad, F.S. Iqtait, M. Alsuhimat, F. |
| author_facet |
Mohamad, F.S. Iqtait, M. Alsuhimat, F. |
| author_sort |
Mohamad, F.S. |
| title |
Age prediction on face features via multiple classifiers |
| title_short |
Age prediction on face features via multiple classifiers |
| title_full |
Age prediction on face features via multiple classifiers |
| title_fullStr |
Age prediction on face features via multiple classifiers |
| title_full_unstemmed |
Age prediction on face features via multiple classifiers |
| title_sort |
age prediction on face features via multiple classifiers |
| publishDate |
2018 |
| url |
http://eprints.unisza.edu.my/1667/1/FH03-FIK-18-14464.jpg http://eprints.unisza.edu.my/1667/ |
| _version_ |
1684657734362857472 |
| score |
13.251813 |