Face recognition (FR) research and technologies focus largely on capabilities of computer algorithms to match stored, gallery images to digital images acquired from video sequences or still images for use in security and law enforcement venues.
These algorithms try to reverse-engineer the human ability to innately recognize a familiar human face. However, one major aspect of this technology that has yet to be thoroughly explored is the effects of age-related craniofacial morphologic changes using the accuracy and reliability of FR technologies.
We have developed model solutions to age adult images up to 70 years old. Our solution is also capable of de-aging a person’s photo. The models developed follow the natural morphological process of the craniofacial complex. See our technical reports and papers to learn more about human aging and modeling this process.
The Face Aging Group partners with biometric researchers from Carnegie Mellon University, Clemson University, and North Carolina A&T State University, to form an IARPA sponsored Center for Advanced Studies in Identity Sciences (CASIS).