Comparing different classifiers for automatic age estimation
Journal
IEEE Transactions On Systems Man and Cybernetics, Part B
Date Issued
January 30, 2004
DOI
10.1109/TSMCB.2003.817091
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.
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.