Development of Android Application for Facial Age Group Classification Using TensorFlow Lite

Authors

  • Gilbert George Department of Computer Science, Baze University, Abuja, Nigeria
  • Steve Adeshina Department of Engineering, Nile University, Abuja, Nigeria.
  • Moussa Mahamat Boukar Department of Computer Science, Nile University, Abuja, Nigeria.

Keywords:

Android, age group classification, convolution neural network, deep learning, still image

Abstract

The concept behind the face age classification system is that every person has a distinct ethnicity and face. An individual's face has numerous distinctive structures and traits, much like their fingerprint. The task of determining facial age is difficult. Systems for facial recognition must function with extreme precision and accuracy, enable lightweight, portable devices, and be user-friendly. In comparison to a situation where just one photograph of each person is saved in the database, images captured while accounting for changing facial expressions or lighting circumstances allow the system to be more precise and accurate. The complete process of creating an Android mobile application for categorizing people as either adults, teenagers, or children is described and explained in full in this article, depending on the traits of their faces. Both the development tools and face classification techniques that have been employed in the creation of Android mobile applications are discussed and explained. The software solution explains the specifics of utilizing the OpenCV library and uses photos to display the actual outcomes of the mobile application.

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Published

21.09.2023

How to Cite

George, G. ., Adeshina, S. ., & Boukar , M. M. . (2023). Development of Android Application for Facial Age Group Classification Using TensorFlow Lite. International Journal of Intelligent Systems and Applications in Engineering, 11(4), 11–17. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3449

Issue

Vol. 11 No. 4 (2023)

Section

Research Article

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