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Age Gap Reducer-GAN for Recognizing Age-Separated Faces

2020-11-11 16:43:32
Daksha Yadav, Naman Kohli, Mayank Vatsa, Richa Singh, Afzel Noore
arXiv_CV
                 
Abstract

In this paper, we propose a novel algorithm for matching faces with temporal variations caused due to age progression. The proposed generative adversarial network algorithm is a unified framework that combines facial age estimation and age-separated face verification. The key idea of this approach is to learn the age variations across time by conditioning the input image on the subject's gender and the target age group to which the face needs to be progressed. The loss function accounts for reducing the age gap between the original image and generated face image as well as preserving the identity. Both visual fidelity and quantitative evaluations demonstrate the efficacy of the proposed architecture on different facial age databases for age-separated face recognition.

Abstract (translated)

URL

https://arxiv.org/abs/2011.05897

PDF

https://arxiv.org/pdf/2011.05897.pdf

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