Konferenzbeitrag
ChildFace: Gender Aware Child Face Aging
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Datum
2020
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Verlag
Gesellschaft für Informatik e.V.
Zusammenfassung
Child face aging and rejuvenation has amassed considerable active research interest due
to its immense impact on monitoring applications especially for finding lost/abducted children with
childhood photos and hence protect children. Prior studies are primarily motivated to enhance the
generation quality and aging of face images, rather than quantifying face recognition performance.
To address this challenge we propose ChildFace model. Our model does child face aging and rejuvenation
while using gender as condition. Our model uses Conditional Generative Adversarial Nets
(cGANs), VGG19 based perceptual loss and LightCNN29 age classifier and produces impressive
results. Intense quantitative study based on verification, identification and age estimation proves that
our model is competent to existing state-of-art models and can make a significant contribution in
identifying missing children.