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.
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机译：近年来，基于面部图像的年龄估计研究越来越受到关注，它包括特征提取和估计函数学习两个过程。在人脸特征提取方面，本文利用卷积神经网络在图像应用领域的优异特性，通过深度学习方法提取人脸特征，并采用因子分析模型提取鲁棒特征。从年龄估计功能学习的角度出发，利用基于年龄和基于等级的年龄估计学习方法的顺序研究方法，提出了划分规则的面部年龄估计器。在FG-NET，MORPH Album 2和IMDB-WIKI上进行的实验表明，特征提取方法比传统的年龄特征提取方法更健壮，并且分割规则估计器的性能优于传统的SVM和SVR。
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