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Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning

Authors:

Aikaterini Kanta,

Brett A. Becker,

Elias Bou-Harb,

Nhien-An Le-Khac,

Mark ScanlonAuthors Info & Claims

ARES '19: Proceedings of the 14th International Conference on Availability, Reliability and Security

Article No.: 57, Pages 1 - 8

https://doi.org/10.1145/3339252.3341491

Published: 26 August 2019 Publication History

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, How-Old.net and DEX.

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Cited By

Deeb-Swihart JEndert ABruckman A(2022)Ethical Tensions in Applications of AI for Addressing Human Trafficking: A Human Rights PerspectiveProceedings of the ACM on Human-Computer Interaction10.1145/35551866:CSCW2(1-29)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555186
Achicanoy HChaves DTrujillo M(2021)StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial ApplicationsSymmetry10.3390/sym1308149713:8(1497)Online publication date: 16-Aug-2021
https://doi.org/10.3390/sym13081497
Anda FDixon EBou-Harb ELe-Khac NScanlon M(2021)Vec2UAge: Enhancing underage age estimation performance through facial embeddingsForensic Science International: Digital Investigation10.1016/j.fsidi.2021.301119(301119)Online publication date: Mar-2021
https://doi.org/10.1016/j.fsidi.2021.301119
Show More Cited By

Index Terms

Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning
1. Applied computing
  1. Computer forensics
    1. Evidence collection, storage and analysis
    2. Investigation techniques
2. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods

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ARES '19: Proceedings of the 14th International Conference on Availability, Reliability and Security

August 2019

979 pages

ISBN:9781450371643

DOI:10.1145/3339252

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 26 August 2019

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ARES '19

ARES '19: 14th International Conference on Availability, Reliability and Security

August 26 - 29, 2019

CA, Canterbury, United Kingdom

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Overall Acceptance Rate 228 of 451 submissions, 51%

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Cited By

Deeb-Swihart JEndert ABruckman A(2022)Ethical Tensions in Applications of AI for Addressing Human Trafficking: A Human Rights PerspectiveProceedings of the ACM on Human-Computer Interaction10.1145/35551866:CSCW2(1-29)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555186
Achicanoy HChaves DTrujillo M(2021)StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial ApplicationsSymmetry10.3390/sym1308149713:8(1497)Online publication date: 16-Aug-2021
https://doi.org/10.3390/sym13081497
Anda FDixon EBou-Harb ELe-Khac NScanlon M(2021)Vec2UAge: Enhancing underage age estimation performance through facial embeddingsForensic Science International: Digital Investigation10.1016/j.fsidi.2021.301119(301119)Online publication date: Mar-2021
https://doi.org/10.1016/j.fsidi.2021.301119
Blanco-Medina PFidalgo EAlegre EAlaiz-Rodríguez RJáñez-Martino FBonnici A(2020)Rectification and Super-Resolution Enhancements for Forensic Text RecognitionSensors10.3390/s2020585020:20(5850)Online publication date: 16-Oct-2020
https://doi.org/10.3390/s20205850
Chaves DFidalgo EAlegre EAlaiz-Rodríguez RJáñez-Martino FAzzopardi G(2020)Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic ApplicationsSensors10.3390/s2016449120:16(4491)Online publication date: 11-Aug-2020
https://doi.org/10.3390/s20164491
Anda FBecker BLillis DLe-Khac NScanlon M(2020)Assessing the Influencing Factors on the Accuracy of Underage Facial Age Estimation2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security)10.1109/CyberSecurity49315.2020.9138851(1-8)Online publication date: Jun-2020
https://doi.org/10.1109/CyberSecurity49315.2020.9138851
Anda FLe-Khac NScanlon M(2020)DeepUAge: Improving Underage Age Estimation Accuracy to Aid CSEM InvestigationForensic Science International: Digital Investigation10.1016/j.fsidi.2020.30092132(300921)Online publication date: Apr-2020
https://doi.org/10.1016/j.fsidi.2020.300921

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