David Lillis: Evaluating Automated Facial Age Estimation Techniques for Digital Forensics

Evaluating Automated Facial Age Estimation Techniques for Digital Forensics

Felix Anda, David Lillis, Nhien-An Le-Khac and Mark Scanlon

In 12th International Workshop on Systematic Approaches to Digital Forensics Engineering (SADFE), 2018 IEEE Security & Privacy Workshops, pages 129--139, San Francisco, CA, USA, 2018. IEEE.

Abstract

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. In certain circumstances, the age of the victim can result in the determination of the crime's categorization, e.g., child abuse investigations. Various automated machine learning-based techniques have been implemented for the analysis of digital images to detect soft biometric traits, such as age and gender, and thus aid detectives and investigators in progressing their cases. This paper documents an evaluation of existing cognitive age prediction services. The evaluative and comparative analysis of the various services was conducted to identify trends and issues inherent to their performance. One significant contributing factor impeding the accurate development of the services investigated is the notable lack of sufficient sample images in specific age ranges, i.e., underage and elderly. To overcome this issue, a dataset generator was developed, which harnesses collections of several unbalanced datasets and forms a balanced, curated dataset of digital images annotated with their corresponding age and gender.