Controllable Face Aging
Haien Zeng, Hanjiang Lai, Jian Yin

TL;DR
This paper introduces a controllable face aging method using an attribute disentanglement GAN that allows fine-grained control over aging effects while preserving key facial attributes like race and gender.
Contribution
It proposes a novel attribute disentanglement GAN framework enabling explicit control over facial attributes during aging, improving flexibility over existing methods.
Findings
Achieves comparable aging effects to state-of-the-art methods.
Provides enhanced control over facial attribute manipulation.
Demonstrates effectiveness on benchmark datasets.
Abstract
Motivated by the following two observations: 1) people are aging differently under different conditions for changeable facial attributes, e.g., skin color may become darker when working outside, and 2) it needs to keep some unchanged facial attributes during the aging process, e.g., race and gender, we propose a controllable face aging method via attribute disentanglement generative adversarial network. To offer fine control over the synthesized face images, first, an individual embedding of the face is directly learned from an image that contains the desired facial attribute. Second, since the image may contain other unwanted attributes, an attribute disentanglement network is used to separate the individual embedding and learn the common embedding that contains information about the face attribute (e.g., race). With the common embedding, we can manipulate the generated face image with…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Face recognition and analysis · Advanced Image Processing Techniques
