Child GAN: Face Aging and Rejuvenation to Find Missing Children

Abstract

The Child GAN project seeks in order to resolve the crucial problem of missing child location by utilizing state-of-the art machine learning methods, particularly Generative Adversarial Networks (GANs). The project's main goal is to create a novel method that uses face aging and rejuvenation algorithms to create age- progressed images of missing children. Our GAN-based model learns complex patterns of facial aging and rejuvenation by utilizing large datasets of facial images taken at different ages. Over time, the model can produce realistic representations of how missing children might age or look rejuvenated by training on pairs of images that show individuals at different stages of life. The age-progressed images that are produced are extremely useful resources for communities, non-profits, and law enforcement agencies that are looking for missing children. Through the use of various media channels, such as social media and traditional media, we hope to increase the visibility of these images of missing children and expedite their prompt recovery.