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

Abstract

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.

Ethnic Representation Matters: Investigating Bias in Facial Age Prediction Models

“…Amelia and Wahyono (2022) [15] also did not explicitly focus on addressing bias and fairness, or the impact of unbalanced training data on model performance. Their primary focus is on improving the accuracy of age estimation using texture-based features and Support Vector Regression (SVR).…”