Publications

Vec2UAge: Enhancing underage age estimation performance through facial embeddings

Published in Forensic Science International: Digital Investigation, 2021

Automated facial age estimation has drawn increasing attention in recent years. Several applications relevant to digital forensic investigations include the identification of victims, suspects and missing children, and the decrease of investigators’ exposure to psychologically impacting material

Recommended citation: F Anda, E Dixon, E Bou-Harb, NA Le-Khac, M Scanlon. (2021). "Vec2UAge: Enhancing underage age estimation performance through facial embeddings." Forensic Science International: Digital Investigation. 36, 301119. http://4nd4.github.io/files/1-s2.0-S2666281721000160.pdf

DeepUAge: improving underage age estimation accuracy to aid CSEM investigation

Published in Forensic Science International: Digital Investigation, 2020

Age is a soft biometric trait that can aid law enforcement in the identification of victims of Child Sexual Exploitation Material (CSEM) creation/distribution. Accurate age estimation of subjects can classify explicit content possession as illegal during an investigation.

Recommended citation: F Anda, NA Le-Khac, M Scanlon. (2020). "DeepUAge: improving underage age estimation accuracy to aid CSEM investigation." Forensic Science International: Digital Investigation. 32, 300921. http://4nd4.github.io/files/1-s2.0-S2666281720300160.pdf

Evaluating automated facial age estimation techniques for digital forensics

Published in 2018 IEEE Security and Privacy Workshops (SPW), 2018

In today’s world, closed circuit television, cellphone photographs and videos, open-source intelligence (i.e., social media/web data mining), and other sources of photographic evidence are commonly used by police forces to identify suspects and victims of both online and offline crimes. Human characteristics, such as age, height, weight, gender, hair color, etc., are often used by police officers and witnesses in their description of unidentified suspects.

Recommended citation: F Anda, D Lillis, NA Le-Khac, M Scanlon. (2018). "Evaluating automated facial age estimation techniques for digital forensics." 2018 IEEE Security and Privacy Workshops (SPW). 129-139. http://4nd4.github.io/files/EvaluatingFacialAge.pdf