Comparing different classifiers for automatic age estimation

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
Andreas LanitisChris Christodoulou

Abstract

We describe a quantitative evaluation of the performance of different classifiers in the task of automatic age estimation. In this context, we generate a statistical model of facial appearance, which is subsequently used as the basis for obtaining a compact parametric description of face images. The aim of our work is to design classifiers that accept the model-based representation of unseen images and produce an estimate of the age of the person in the corresponding face image. For this application, we have tested different classifiers: a classifier based on the use of quadratic functions for modeling the relationship between face model parameters and age, a shortest distance classifier, and artificial neural network based classifiers. We also describe variations to the basic method where we use age-specific and/or appearance specific age estimation methods. In this context, we use age estimation classifiers for each age group and/or classifiers for different clusters of subjects within our training set. In those cases, part of the classification procedure is devoted to choosing the most appropriate classifier for the subject/age range in question, so that more accurate age estimates can be obtained. We also present comparativ...Continue Reading

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