Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning

Title: Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning
Authors: Anda, FelixLillis, DavidKanta, AikateriniBecker, Brett A.Bou-Harb, EliasLe-Khac, Nhien-AnScanlon, Mark
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Date: 26-Aug-2019
Online since: 2020-04-07T14:10:59Z
Abstract: Achieving high performance for facial age estimation with subjects in the borderline between adulthood and non-adulthood has always been a challenge. Several studies have used different approaches from the age of a baby to an elder adult and different datasets have been employed to measure the mean absolute error (MAE) ranging between 1.47 to 8 years. The weakness of the algorithms specifically in the borderline has been a motivation for this paper. In our approach, we have developed an ensemble technique that improves the accuracy of underage estimation in conjunction with our deep learning model (DS13K) that has been fine-tuned on the Deep Expectation (DEX) model. We have achieved an accuracy of 68% for the age group 16 to 17 years old, which is 4 times better than the DEX accuracy for such age range. We also present an evaluation of existing cloud-based and offline facial age prediction services, such as Amazon Rekognition, Microsoft Azure Cognitive Services, and DEX.
Type of material: Conference Publication
Publisher: ACM
Keywords: Underage photo datasetsDeep learningDigital forensicsChild exploitation investigationFacial recognition
DOI: 10.1145/3339252.3341491
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Language: en
Status of Item: Peer reviewed
Is part of: ARES '19: Proceedings of the 14th International Conference on Availability, Reliability and Security
Conference Details: The 14th International Conference on Availability, Reliability and Security (ARES 2019), Canterbury, United Kingdom, 26-29 August 2019
ISBN: 9781450371643
Appears in Collections:Computer Science Research Collection

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