Age Gap Reducer-GAN for Recognizing Age-Separated Faces
This addresses age-separated face recognition, an incremental improvement for applications like security and forensics.
The paper tackles the problem of matching faces across age progression by proposing a GAN-based algorithm that conditions on gender and target age to reduce the age gap while preserving identity, achieving efficacy demonstrated through visual and quantitative evaluations on facial age databases.
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.