Age detection from face using Convolutional Neural Network (CNN) / Hanin Hanisah Usok @ Yusoff - Usok @ Yusoff, Hanin Hanisah (2024)
Face-based age recognition has significant effects for a variety of purposes, including personalised services and security measures. The capacity to reliably estimate a person's age using facial traits is critical in improving user experiences and security processes. In this project, we want to create an age identification system that uses Convolutional Neural Network (CNN) algorithms to estimate people's ages fast and accurately from facial images. Following a thorough examination of numerous algorithms, CNN was determined to be the best effective method for age recognition from facial features due to its ability to automatically extract important data. The CNN model is thoroughly trained and analysed on various kinds of datasets containing facial photos of different ages. The results show a high 85% accuracy rate in determining the age of individuals. A user-friendly desktop system is created for input of facial photos and receiving immediate age estimation results, illustrating machine learning's assure in age identification. With implications for personalised services and security, this experiment demonstrates how CNN algorithms improve accuracy, adding to successful age-related technology.