Open AccessAge Group Classification using Convolutional Neural Network (CNN)
Author(s) -
Muhammad Firdaus Mustapha,
Nur Maisarah Mohamad,
Ghazali Osman,
Siti Haslini Ab Hamid
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2084/1/012028
Subject(s) - convolutional neural network , artificial intelligence , computer science , task (project management) , face (sociological concept) , pattern recognition (psychology) , deep learning , group (periodic table) , embedding , age groups , machine learning , social science , chemistry , demography , organic chemistry , sociology , management , economics
Age group classification is a complex task that is used to classify facial images or videos into predetermined age categories. It is an important task due to its numerous applications such as health, security, authentication system, recruitment, and also in intelligent social robots. Convolutional Neural Network (CNN) has recently shown excellent performance in analysing human face images and videos. This paper proposed an age group classification task using CNN that trained and tested with an All-Age Face (AAF) dataset. FaceNet deep learning model that uses CNN was applied in this study to compute a 128-d embedding that quantifies the face of the age group. The experiment included two age groups: Adolescence and Mature Adulthood. The proposed age group classification model achieved 84.90% accuracy for the training images and 85.12% accuracy for the test images. The experimental results showed that CNN is capable of achieving competitive classification accuracy throughout two age groups in the AAF dataset with unbalanced data distribution.