Face Aging With Conditional Generative Adversarial Networks
This work addresses the challenge of identity preservation in face aging for applications like entertainment or forensics, representing an incremental improvement over previous GAN-based attribute alteration methods.
The authors tackled the problem of generating realistic aged face images while preserving the person's identity, using a GAN-based method with identity-preserving optimization, achieving high performance as validated by state-of-the-art face recognition and age estimation tools.
It has been recently shown that Generative Adversarial Networks (GANs) can produce synthetic images of exceptional visual fidelity. In this work, we propose the GAN-based method for automatic face aging. Contrary to previous works employing GANs for altering of facial attributes, we make a particular emphasize on preserving the original person's identity in the aged version of his/her face. To this end, we introduce a novel approach for "Identity-Preserving" optimization of GAN's latent vectors. The objective evaluation of the resulting aged and rejuvenated face images by the state-of-the-art face recognition and age estimation solutions demonstrate the high potential of the proposed method.