Anda, FelixFelixAndaBecker, Brett A.Brett A.BeckerLillis, DavidDavidLillisLe-Khac, Nhien-AnNhien-AnLe-KhacScanlon, MarkMarkScanlon2023-09-202023-09-202020 IEEE2020-06-199781728164281http://hdl.handle.net/10197/24775The IEEE International Conference on Cyber Security and Protection of Digital Services (Cyber Security 2020), Dublin, Ireland, 15-19 June 2020Swift 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.en© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Cloud computingFace recognitionLearning (artificial intelligence)Police data processingWeb servicesConference Publication10.1109/CyberSecurity49315.2020.91388512020-12-02https://creativecommons.org/licenses/by-nc-nd/3.0/ie/