An Overview of Two Age Synthesis and Estimation Techniques
It addresses the need for systematic insights into age-related facial analysis techniques for researchers in computer vision and face verification systems, but is incremental as a review.
This paper provides an overview of existing models, techniques, and challenges in facial image-based age synthesis and estimation, aiming to facilitate understanding and suggest future directions in computer vision.
Age estimation is a technique for predicting human ages from digital facial images, which analyzes a person's face image and estimates his/her age based on the year measure. Nowadays, intelligent age estimation and age synthesis have become particularly prevalent research topics in computer vision and face verification systems. Age synthesis is defined to render a facial image aesthetically with rejuvenating and natural aging effects on the person's face. Age estimation is defined to label a facial image automatically with the age group (year range) or the exact age (year) of the person's face. In this case study, we overview the existing models, popular techniques, system performances, and technical challenges related to the facial image-based age synthesis and estimation topics. The main goal of this review is to provide an easy understanding and promising future directions with systematic discussions.