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.
Highlights
In the medical profession, people rely mainly on the analysis of cholesterol, high density cholesterol, albumin, and other blood test indicators to determine a person’s “physiological age” and to study the degree of human aging
We compare the novel face feature extraction method based on deep learning (DLF + factor analysis model (FAM)) with Gabor + principal component analysis (PCA), LBP + PCA, Gabor + LBP + PCA, and Bioinspired Features (BIF) + PCA, which are the common face feature extraction methods
A robust face age feature extraction method is proposed based on the superior image representation ability of depth convolution neural network
Summary
People rely mainly on the analysis of cholesterol, high density cholesterol, albumin, and other blood test indicators to determine a person’s “physiological age” and to study the degree of human aging. This set of indicators is still very imperfect and of great inconvenience to use. If we can use computer and image processing technology to analyze facial images to accurately predict a person’s “physiological age” and compare “physiological age” and “actual age”, we can know whether you are in “Youth Permanent” or “Premature Aging”. Humans quickly estimate each other’s gender, age, and identity through the appearance of the other person’s face in order to select different social styles
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