Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/1112
Title: Comparing different classifiers for automatic age estimation
Authors: Draganova, Chrisina
Christodoulou, Chris
Lanitis, Andreas 
Keywords: Aging
Face recognition
Image classification
Neural networks
Issue Date: 2004
Publisher: IEEE
Source: IEEE Transactions On Systems Man and Cybernetics, Part B, Vol 34, No 1, pp 621-629, 2004
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 comparative results concerning the performance of humans and computers in the task of age estimation. Our results indicate that machines can estimate the age of a person almost as reliably as humans.
URI: http://ktisis.cut.ac.cy/handle/10488/1112
ISSN: 1083-4419
DOI: 10.1109/TSMCB.2003.817091
Rights: © 2004 IEEE
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