Open AccessFace Aging on Realistic Photo in Cross-Dataset Implementation
Author(s) -
N. Hamzah,
Fadhlan Hafizhelmi Kamaru Zaman
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/917/1/012080
Subject(s) - face (sociological concept) , image translation , translation (biology) , computer science , image (mathematics) , artificial intelligence , computer vision , process (computing) , generative grammar , pattern recognition (psychology) , social science , biochemistry , chemistry , sociology , messenger rna , gene , operating system
Face aging or age progression is a prediction on how a person looks at the future. Face aging image-to-image translation is a process of translating an image of young people to their older version or vice versa. The need for a paired training dataset to train the generative adversarial networks (GANs) is a major problem with face aging image-to-image translation. Nowadays, there is a method where an unpaired training dataset can be used to do an image-to-image translation. CycleGANs is a GANs extended methods where there is no need for paired training dataset to train the CycleGANs. From the result, it shows that CycleGANs can do face aging image-to-image translation without using the paired training dataset.