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2019 | OriginalPaper | Chapter

Real-Time Age Detection Using a Convolutional Neural Network

Authors : Siphesihle Sithungu, Dustin Van der Haar

Published in: Business Information Systems

Publisher: Springer International Publishing

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Abstract

The problem of determining people’s age is a recurring theme in areas such as law enforcement, education and sports because age is often used to determine eligibility. The aim of current work is to make use of a lightweight machine learning model for automating the task of detecting people’s age. This paper presents a solution that makes use of a lightweight Convolutional Neural Network model, built according to a modification of the LeNet-5 architecture to perform age detection, for both males and females, in real-time. The UTK-Face Large Scale Face Dataset was used to train and test the performance of the model in terms of predicting age. To evaluate the model’s performance in real-time, Haar Cascades were used to detect faces from video feeds. The detected faces were fed to the model for it to make age predictions. Experimental results showed that age-detection can be performed in real-time. Although, the prediction accuracy of the model requires improvement.

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Title: Real-Time Age Detection Using a Convolutional Neural Network
Authors: Siphesihle Sithungu
Dustin Van der Haar
Publisher: Springer International Publishing
Book: Business Information Systems
Print ISBN: 978-3-030-20481-5

Electronic ISBN: 978-3-030-20482-2

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-20482-2_20

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