Paper Info
Title
Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning
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.
Year
DOI
Venue
2019
10.1145/3339252.3341491
Proceedings of the 14th International Conference on Availability, Reliability and Security
Keywords
Field
DocType
Child Exploitation Investigation, Deep Learning, Digital Forensics, Facial Recognition, Underage Photo Datasets
Computer science,Mean absolute error,Artificial intelligence,Deep learning,Cognition,Ensemble learning,Machine learning,Cloud computing
Conference
ISSN
ISBN
Citations 
14th International Conference on Availability, Reliability and Security (ARES 2019), Canterbury, UK, August 2019
978-1-4503-7164-3
0
PageRank 
References 
Authors
0.34
16
7
Name
Order
Citations
PageRank
Felix Anda122.44
David Lillis210817.43
Aikaterini Kanta301.01
Brett A. Becker49326.00
Elias Bou-Harb520726.40
Nhien-An Le-Khac622449.63
Mark Scanlon72310.74