Assessing the Influencing Factors on the Accuracy of Underage Facial Age Estimation

TL;DR

This study evaluates how biometric factors, facial expressions, and image quality affect the accuracy of automated underage facial age estimation services, aiming to improve future system robustness for law enforcement use.

Contribution

It provides a comprehensive assessment of two major cloud-based age estimation services on a large underage dataset, highlighting key factors influencing accuracy.

Findings

Facial expressions significantly impact age estimation accuracy.

Image quality issues like blur and noise reduce reliability.

Biometric and image factors are critical for improving automated age estimation.

Abstract

Swift response to the detection of endangered minors is an ongoing concern for law enforcement. Many child-focused investigations hinge on digital evidence discovery and analysis. Automated age estimation techniques are needed to aid in these investigations to expedite this evidence discovery process, and decrease investigator exposure to traumatic material. Automated techniques also show promise in decreasing the overflowing backlog of evidence obtained from increasing numbers of devices and online services. A lack of sufficient training data combined with natural human variance has been long hindering accurate automated age estimation -- especially for underage subjects. This paper presented a comprehensive evaluation of the performance of two cloud age estimation services (Amazon Web Service's Rekognition service and Microsoft Azure's Face API) against a dataset of over 21,800 underage subjects. The objective of this work is to evaluate the influence that certain human biometric factors, facial expressions, and image quality (i.e. blur, noise, exposure and resolution) have on the outcome of automated age estimation services. A thorough evaluation allows us to identify the most influential factors to be overcome in future age estimation systems.