08-16-2017, 10:39 PM
There has been a growing interest in automatic age estimation
from facial images due to a variety of potential applications
in law enforcement, security control, and humancomputer
interaction. However, despite advances in automatic
age estimation, it remains a challenging problem.
This is because the face aging process is determined not
only by intrinsic factors, e.g. genetic factors, but also by extrinsic
factors, e.g. lifestyle, expression, and environment.
As a result, different people with the same age can have
quite different appearances due to different rates of facial
aging. We propose a hierarchical approach for automatic
age estimation, and provide an analysis of how aging influences
individual facial components. Experimental results
on the FG-NET, MORPH Album2, and PCSO databases
show that eyes and nose are more informative than the
other facial components in automatic age estimation. We
also study the ability of humans to estimate age using data
collected via crowdsourcing, and show that the cumulative
score (CS) within 5-year mean absolute error (MAE) of our
method is better than the age estimates provided by humans.
