Age Estimation from Facial Images Using Custom Convolutional Neural Network (CNN)

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  • Gilbert George Nile University
  • Steve Adeshina Nile University
  • Moussa Mahamat Boukar Nile University


Artificial Neural Network, Computer Vison, Convolutional Neural Network, Facial Age Classification


Given that aging is influenced by a variety of factors, including gender, ethnicity, environment, and others, automatic age assessment of facial images is a difficult challenge in computer vision and image analysis. Additionally, a significant amount of data and a laborious training phase are needed to estimate age from facial photos with near accuracy. In this study, we present a custom convolutional neural network-based age estimator that can almost precisely predict age from facial photos. We use the UTK facial image dataset using about 17475 images. We train the model to group the facial images into three groups which are; Child, Teenager and Adult. Compared to similar efforts, our method uses less training data while maintaining a high accuracy of 95%.

Author Biographies

Gilbert George, Nile University

Department of Computer Science, Nigeria

Steve Adeshina, Nile University

Department of Engineering, Nigeria

Moussa Mahamat Boukar, Nile University

Department of Computer Science, Nigeria


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How to Cite

George, G., Adeshina, S., & Boukar, M. M. (2023). Age Estimation from Facial Images Using Custom Convolutional Neural Network (CNN). International Conference on Frontiers in Academic Research, 1, 134–137. Retrieved from