Age Gap Reducer-GAN for Recognizing Age-Separated Faces

TL;DR

This paper introduces Age Gap Reducer-GAN, a novel generative adversarial network that improves age-separated face recognition by reducing age gaps while preserving identity, combining age estimation and face verification.

Contribution

The paper presents a unified GAN framework that conditions on gender and target age to generate age-progressed faces, enhancing recognition across age gaps.

Findings

Effective in reducing age gaps in face recognition

Preserves identity during age progression

Demonstrates superior performance on multiple datasets

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.