# Age Gap Reducer-GAN for Recognizing Age-Separated Faces

arXiv: https://arxiv.org/abs/2011.05897 · HTML: https://mlsanity.com/paper/2011.05897/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Inclusive Review on Advances in Masked Human Face Recognition Technologies](https://mlsanity.com/paper/2508.00841/) (2025)

## Abstract (arXiv)

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.