Original scientific paper

https://doi.org/10.17559/TV-20170308030459

The Integrated Usage of LBP and HOG Transformations and Machine Learning Algorithms for Age Range Prediction from Facial Images

Tariq Khalifa orcid.org/0000-0003-4246-9846 ; Al-Jabal Al-Gharbi University, 64200, Gharian, Libya
Gokhan Sengul orcid.org/0000-0003-2273-4411 ; Atilim University, Kızılcaşar Mahallesi, İncek / Ankara 06836, Turkey

Abstract

Age prediction is an active study field that can be used in many computer vision problems due to its importance and effectiveness. In this paper, we present extensive experiments and provide an efficient and accurate approach for age range prediction of people from facial images. First, we apply image resizing to unify all images’ size, and Histogram Equalization technique to reduce the illumination effects on all facial images taken from FG-NET and UTD aging databases. Second, Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are used to extract the features of these images, and then we combined both HOG and LBP features in order to attain better prediction. Finally, Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) are used for the classification processes. In addition, k-fold, Leave-One-Out (LOO) and Confusion Matrix (CM) are used to evaluate the performance of proposed methods. The extensive and intensified experiments show that combining HOG and LBP features improved the age range predicting performance up to 99.87%.

Keywords

age prediction; facial images; Histogram of Oriented Gradient (HOG); kNN; Local Binary Pattern (LBP); SVM

Hrčak ID:

207433

URI

https://hrcak.srce.hr/207433

Publication date:

28.10.2018.

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