A Review on the Suitability of Machine Learning Approaches to Facial Age Estimation

Abstract

Age is a human attribute which grows alongside an individual. Estimating human age is quite difficult for machine as well as humans, however there has been and are still ongoing efforts towards machine estimation of human age to a high level of accuracy. In a bid to improve the accuracy of age estimation from facial image, several approaches have been proposed many of which used Machine Learning algorithms. The several Machine Learning algorithms employed in these works have made significant impact on the results and of performances of the proposed age estimation approaches. In this paper, we examined and compared the performance of a number of Machine Learning algorithms used for age estimation in several previous works. Considering two publicly available facial ageing datasets (FG-NET and MORPH) which have been mostly used in previous works, we observed that Support Vector Machine (SVM) has been most popularly used and a combination/hybridization of SVM for classification (SVC) and regression (SVR) have shown the best performance so far. We also observed that the face modelling or feature extraction techniques employed significantly impacted the performance of age estimation