Classification of Different Age Groups of People by Using Deep Learning

Year 2018, Volume: 7 Issue: 3, 9 - 16, 15.12.2018

Özkan İnik , Bülent Turan

Abstract

The Purpose of this study is to classify human images of
different age groups with VggNet which is one of the Deep Learning (DL) models.
Artificial intelligence, machine learning and computer vision have been carried
out in recent years at very advanced level. 
Undoubtedly, it is a great contribution of DL in the rapid progress of
these studies. Although DL foundational is based on past history, it has become
popular in the imageNet competition held in 2012. This is because the top-5 error
rate of 26.1% for visual object description has fallen to 15.3% for the first
time with a sharp decline that year with DL. The Convolution Neural Network
(CNN) is basis of DL models. It is basically composed of 4 layers. These are
Convolution Layer, ReLu Layer, Pooling Layer and Full Connected Layer. DL models
are designed using different numbers of these layers. In this study, people are
divided into 12 classes according to age groups. These classes are man, woman,
man face, woman face, old man, old woman, old man face, old woman face, boy,
girl, boy face, girl face respectively. A new data set was created for people
in 12 different age categories. For Each class 150 and totally 1800 images were
collected. 90% of these images were used for training and the remaining 10%
were used for testing. VggNet was trained with this data set. As a result of
the study, it was seen that people in different age groups were estimated with
78.5% accuracy with VggNet model. DL models need to be trained with large data
required. But it has been seen that training success has achieved a certain
value with little data.

Keywords

Classification of people, Deep Learning, VggNet

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