  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.
