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Conference Papers Year : 2017

Face Aging With Conditional Generative Adversarial Networks

Grigory Antipov
Jean-Luc Dugelay
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Abstract

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.

Dates and versions

hal-01617351 , version 1 (16-10-2017)

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Grigory Antipov, Moez Baccouche, Jean-Luc Dugelay. Face Aging With Conditional Generative Adversarial Networks. IEEE International Conference on Image Processing, Sep 2017, Pékin, China. ⟨hal-01617351⟩

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