Age Estimation of Face Images Based on CNN and Divide-and-Rule Strategy

Abstract

In recent years, the research on age estimation based on face images has drawn more and more attention, which includes two processes: feature extraction and estimation function learning. In the aspect of face feature extraction, this paper leverages excellent characteristics of convolution neural network in the field of image application, by using deep learning method to extract face features, and adopts factor analysis model to extract robust features. In terms of age estimation function learning, age-based and sequential study of rank-based age estimation learning methods is utilized and then a divide-and-rule face age estimator is proposed. Experiments in FG-NET, MORPH Album 2, and IMDB-WIKI show that the feature extraction method is more robust than traditional age feature extraction method and the performance of divide-and-rule estimator is superior to classical SVM and SVR.