INSTITUTIONAL DIGITAL REPOSITORY

Cycle face aging generative adversarial networks

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dc.contributor.author Thengane, V.G.
dc.contributor.author Gawande, M.B.
dc.contributor.author Dudhane, A.A.
dc.contributor.author Gonde, A.B.
dc.date.accessioned 2019-08-24T09:48:00Z
dc.date.available 2019-08-24T09:48:00Z
dc.date.issued 2019-08-24
dc.identifier.uri http://localhost:8080/xmlui/handle/123456789/1347
dc.description.abstract The facial features of human changes with age. It is important to model the human face for cross-age verification and recognition. In this paper, we introduce a Cycle Face Aging Generative Adversarial Network (CFA-GANs) framework which preserves original face identity in the aged version of his/her face. Due to the shortage of paired data of human faces, we used CycleConsistent Generative Adversarial Network (CycleGANs) which transform an image from source domain X to target domain Y in absence of paired example. Our aim is to translate an input age group image into target age group image for face aging problems. Train on the various images, we demonstrate that our CFA-GAN learns and transfer the features of the face from the input group to target group. Results have been taken on UTKFace database to obtain aged and regenerated face images. en_US
dc.language.iso en_US en_US
dc.subject Face aging en_US
dc.subject GANs en_US
dc.subject Paired and unpaired data en_US
dc.subject CNN en_US
dc.title Cycle face aging generative adversarial networks en_US
dc.type Article en_US


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