Abstract:
Facial aging is an important yet challenging problem that has recently been
added to the problem of the face recognition. Human face varies over time in
many aspects, including large inter-user similarity such as facial texture
wrinkles, shape weight gain, facial hair, presence of glasses, etc. and large
intra-subject variations such as pose, illumination, expression, and aging.
Age invariant face recognition recently has gained a significant interest
within the image processing and computer vision research community
because of its explosively emerging real-world applications in many areas,
such as forensic art, electronic customer relationship management, security
control and surveillance monitoring, biometrics, entertainment, and
cosmetology. This paper presents a thorough analysis on the problem of
facial aging and further provides a complete account of the many interesting
studies that have been performed on this topic. The face recognition methods
that overcome aging fall into two main categories: generative and non generative. Here we discuss a detail analysis of above two approaches that
have been proposed for this problem and offer insights into future research
on this topic. However, designing an appropriate feature representation and
an effective matching framework for age invariant face recognition remains
an open problem as no reliable and high performing research result is
reportedly implemented. Investigation results related to various illumination
conditions, different expressions, biometric performance issues, etc are not
satisfactory or not available at all.
