Age Estimation on Human Face Image Using Support Vector Regression and Texture-Based Features
This paper proposed a framework for estimating human age using facial features. These features exploit facial region information, such as wrinkles on the eye and cheek, which are then represented as a texture-based feature. Our proposed framework has several steps: preprocessing, feature extraction...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
ProQuest
2022
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/282656/1/Age_Estimation_on_Human_Face_I.pdf |
| _version_ | 1797037579255676928 |
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| author | Amelia, Jesy S. Wahyono, Wahyono |
| author_facet | Amelia, Jesy S. Wahyono, Wahyono |
| author_sort | Amelia, Jesy S. |
| collection | UGM |
| description | This paper proposed a framework for estimating human age using facial features. These features exploit facial
region information, such as wrinkles on the eye and cheek, which are then represented as a texture-based feature. Our proposed framework has several steps: preprocessing, feature extraction, and age estimation. In this research, three feature extraction methods and their combination are performed, such as Local Binary Pattern (LBP), Local Phrase Quantization (LPQ), and Binarized Statistical Image Feature (BSIF). After extracting the feature, Principle Component Analysis (PCA) was performed to reduce the feature size. Finally, the Support Vector Regression (SVR) method was used to predict age. In evaluation, the estimation error will be based on mean average error (MAE). In the experiment, we utilized the well-known public dataset, faceage.zip, and UTK Face datasets, containing 15,202 facial image data. The data were divided into the training of 12,162 images and the testing of 3,040 images. Our experiments found that
combining BSIF and LPQ with PCA achieved the lowest MAE of
9.766 and 9.754. The results show that the texture-bas |
| first_indexed | 2024-03-14T00:05:27Z |
| format | Article |
| id | oai:generic.eprints.org:282656 |
| institution | Universiti Gadjah Mada |
| language | English |
| last_indexed | 2024-03-14T00:05:27Z |
| publishDate | 2022 |
| publisher | ProQuest |
| record_format | dspace |
| spelling | oai:generic.eprints.org:2826562023-11-16T04:23:56Z https://repository.ugm.ac.id/282656/ Age Estimation on Human Face Image Using Support Vector Regression and Texture-Based Features Amelia, Jesy S. Wahyono, Wahyono Image Processing Health Information System This paper proposed a framework for estimating human age using facial features. These features exploit facial region information, such as wrinkles on the eye and cheek, which are then represented as a texture-based feature. Our proposed framework has several steps: preprocessing, feature extraction, and age estimation. In this research, three feature extraction methods and their combination are performed, such as Local Binary Pattern (LBP), Local Phrase Quantization (LPQ), and Binarized Statistical Image Feature (BSIF). After extracting the feature, Principle Component Analysis (PCA) was performed to reduce the feature size. Finally, the Support Vector Regression (SVR) method was used to predict age. In evaluation, the estimation error will be based on mean average error (MAE). In the experiment, we utilized the well-known public dataset, faceage.zip, and UTK Face datasets, containing 15,202 facial image data. The data were divided into the training of 12,162 images and the testing of 3,040 images. Our experiments found that combining BSIF and LPQ with PCA achieved the lowest MAE of 9.766 and 9.754. The results show that the texture-bas ProQuest 2022 Article PeerReviewed application/pdf en https://repository.ugm.ac.id/282656/1/Age_Estimation_on_Human_Face_I.pdf Amelia, Jesy S. and Wahyono, Wahyono (2022) Age Estimation on Human Face Image Using Support Vector Regression and Texture-Based Features. (IJACSA) International Journal of Advanced Computer Science and Applications, 13 (12). pp. 122-129. https://www.proquest.com/docview/2770373835?pq-origsite=gscholar&fromopenview=true 10.14569/IJACSA.2022.0131217 |
| spellingShingle | Image Processing Health Information System Amelia, Jesy S. Wahyono, Wahyono Age Estimation on Human Face Image Using Support Vector Regression and Texture-Based Features |
| title | Age Estimation on Human Face Image Using Support Vector Regression and Texture-Based Features |
| title_full | Age Estimation on Human Face Image Using Support Vector Regression and Texture-Based Features |
| title_fullStr | Age Estimation on Human Face Image Using Support Vector Regression and Texture-Based Features |
| title_full_unstemmed | Age Estimation on Human Face Image Using Support Vector Regression and Texture-Based Features |
| title_short | Age Estimation on Human Face Image Using Support Vector Regression and Texture-Based Features |
| title_sort | age estimation on human face image using support vector regression and texture based features |
| topic | Image Processing Health Information System |
| url | https://repository.ugm.ac.id/282656/1/Age_Estimation_on_Human_Face_I.pdf |
| work_keys_str_mv | AT ameliajesys ageestimationonhumanfaceimageusingsupportvectorregressionandtexturebasedfeatures AT wahyonowahyono ageestimationonhumanfaceimageusingsupportvectorregressionandtexturebasedfeatures |