To minimize the impact of age variation on face recognition, age-invariant face recognition
(AIFR) extracts identity-related discriminative features by minimizing the correlation between …

Photorealistic digital re-aging of faces in video is becoming increasingly common in
entertainment and advertising. But the predominant 2D painting workflow often requires …

Face age transformation aims to synthesize past or future face images by reflecting the age
factor on given faces. Ideally, this task should synthesize natural-looking faces across …

Background The integration of augmented reality (AR) and virtual reality (VR) technologies
into cosmetic dermatology offers new avenues for enhancing patient engagement …

Face Age Progression (FAP) refers to synthesizing face images while simulating ageing
effects, thus enabling predicting the future appearance of an individual. The generation of …

Age transformation aims to preserve personalized facial information while altering a given
face to appear at a target age. This technique finds extensive applications in fields such as …

Many studies have been conducted in the field of face aging, from approaches that use pure
image-processing algorithms, to those that use generative adversarial networks. In this …

Cross aging face recognition ability will decrease to recognize someone's face after a
certain time. Adding synthetics face images at a certain age generated from face aging …

Recently, there is incredible progress in the arena of machine learning with generative
adversarial network (GAN) methods. These methods tend to synthesize new data from input …

The challenge of transforming the apparent age of human faces in videos has not been
adequately addressed due to the complexities involved in preserving spatial and temporal …