A Support Vector Regression based Machine Learning method for on-chip Aging Estimation

Alnuayri, Turki; Martinez, Antonio Leonel Hernandez; Khursheed, Saqib; Rossi, Daniele

doi:10.1109/iccis54243.2021.9676376

Cited by 3 publications

(2 citation statements)

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“…The resizing function is carried out using the interpolation method. The following is a calculation of the resizing process using the linear interpolation formula: (1) where ( o , o ) is two points of initial coordinates, ( 1 , 1 ) is destination coordinates, and ( , ) is coordinate prediction.…”

Section: A Preprocessingmentioning

confidence: 99%

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Age Estimation on Human Face Image Using Support Vector Regression and Texture-Based Features

Amelia¹,

Wahyono²

2022

IJACSA

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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-based feature could be utilized for estimating the age on facial image.

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Section: A Preprocessingmentioning

confidence: 99%

“…In addition, Support Vector Regression (SVR) was used in the age estimation stage. SVR is a machine learning algorithm that provides reliable performance in predicting time series data and can classify data that cannot be separated linearly [1].…”

Section: Introductionmentioning

confidence: 99%