Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Our world
today is full of development and people have become obsessed with beauty and
maintaining the continuity of youth and fear of facial aging, which represents
gradual changes in physiological functions. Hence, the need to simulate facial
aging has emerged. Recently, many contributions have spread that have addressed
this phenomenon. In this paper, we highlight many facial aging methodologies:
the traditional approach, the deep learning approach, and the hybrid model
approach. Regarding the feature-based approach, we will explain its strengths
and weaknesses. Then we will highlight the methodologies based on deep
learning, especially the competitive generative networks that have improved the
accuracy and realism of facial aging images. We will also explain the
strengths, weaknesses, and possible improvement. In addition, we will mention
the hybrid models and their strengths and weaknesses. The datasets used in this
work are presented. Furthermore, we will explain how to deal with the
challenges associated with facial aging estimation, starting with the obstacles
that accompany the dataset, warnings about privacy and ethical considerations,
with the need to address these challenges to ensure the safe and ethical use of
the techniques, and discuss the impact of the unbalanced distribution of the
dataset, and touch on some of the criteria for evaluating these methods.
Finally, we present a future vision on the emerging trends, challenges and
future directions in the field of facial aging in order to help guide future
research in the right direction.
Country : Iraq
IRJIET, Volume 9, Issue 4, April 2025 pp. 33-49