Graduate School and Research Center in Digital Sciences

Face aging with conditional generative adversarial networks

Antipov, Grigory; Baccouche, Moez; Dugelay, Jean-Luc

ICIP 2017, IEEE International Conference on Image Processing, 17-20 September 2017, Beijing, China / Also on ArXiv

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-theart face recognition and age estimation solutions demonstrate the high potential of the proposed method.

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Title:Face aging with conditional generative adversarial networks
Keywords:Face Aging, GAN, Deep Learning, Face Synthesis
Department:Digital Security
Eurecom ref:5134
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Bibtex: @inproceedings{EURECOM+5134, doi = {}, year = {2017}, title = {{F}ace aging with conditional generative adversarial networks}, author = {{A}ntipov, {G}rigory and {B}accouche, {M}oez and {D}ugelay, {J}ean-{L}uc}, booktitle = {{ICIP} 2017, {IEEE} {I}nternational {C}onference on {I}mage {P}rocessing, 17-20 {S}eptember 2017, {B}eijing, {C}hina / {A}lso on {A}r{X}iv}, address = {{B}eijing, {CHINA}}, month = {09}, url = {} }
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