Authors :
Y.V. Ragavendra Reddy; P. Kalaiarasi; M. Tejeswara Reddy; K. Sri Charan Reddy; V. Ajay Raj
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/3amfcccd
Scribd :
https://tinyurl.com/259bhhxe
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR1378
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.
Keywords :
Child GAN, Face Rejuvenation, Face Aging Missing Kids, Age Progression, Facial Recognition, Machine Learning, and GANs (Generative Adversarial Networks) Gathering of Datasets, Moral Considerations.
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.
Keywords :
Child GAN, Face Rejuvenation, Face Aging Missing Kids, Age Progression, Facial Recognition, Machine Learning, and GANs (Generative Adversarial Networks) Gathering of Datasets, Moral Considerations.
