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Real-Time Age Detection Using a Convolutional Neural Network

  • Siphesihle Sithungu
  • Dustin Van der HaarEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 354)

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.

Keywords

Age detection Convolutional Neural Network Computer vision Machine learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Academy of Computer Science and Software EngineeringUniversity of JohannesburgJohannesburgSouth Africa

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