Automatic Age Progression: Methodologies and Performance Evaluation
Date Issued
2008
Author(s)
Abstract
Age progression is the process of predicting the future facial appearance of faces
appearing in images. The ability to produce accurate age progressed faces is important
in a number of key applications including the identification of missing persons,
development of age-invariant face recognition systems and automatic update of
photographs in smart documents. Automatic age-progression is a challenging task,
mainly due to the diversity of aging variation and the dependence of the aging process
on external factors that include health conditions and hereditary trends.
Recently an escalated research interest in the area is recorded and as a result a number
of face aging algorithms were reported in the literature. In this paper we present key
techniques reported in the literature with emphasis on techniques developed by the
author. In particular we present a face-aging algorithm based on the definition of
aging trajectories (the so called aging functions) and algorithms based on modelling
the distributions of faces belonging to different subjects and different age groups.
Special emphasis is given to the topic of performance evaluation of age progression
methodologies based on dedicated metrics. Work in this area is considered an
important research direction in the field, since it will enable the evolution of the most
promising age progression methodologies.
appearing in images. The ability to produce accurate age progressed faces is important
in a number of key applications including the identification of missing persons,
development of age-invariant face recognition systems and automatic update of
photographs in smart documents. Automatic age-progression is a challenging task,
mainly due to the diversity of aging variation and the dependence of the aging process
on external factors that include health conditions and hereditary trends.
Recently an escalated research interest in the area is recorded and as a result a number
of face aging algorithms were reported in the literature. In this paper we present key
techniques reported in the literature with emphasis on techniques developed by the
author. In particular we present a face-aging algorithm based on the definition of
aging trajectories (the so called aging functions) and algorithms based on modelling
the distributions of faces belonging to different subjects and different age groups.
Special emphasis is given to the topic of performance evaluation of age progression
methodologies based on dedicated metrics. Work in this area is considered an
important research direction in the field, since it will enable the evolution of the most
promising age progression methodologies.
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