Age Detection Using Deep Learning Techniques: A Comparative Study of CNN, VGG19, and ResNet.

This research focuses on developing an automated system to estimate human age using facial images based on deep learning algorithms. The study compares the performance of three major architectures: Convolutional Neural Network (CNN), VGG19, and ResNet. A dataset consisting of 13,300 facial images was collected from open-source platforms such as Kaggle and Google, divided into nine distinct age categories. After extensive preprocessing and training using optimizers Adam and RMSProp, the VGG19 model achieved the highest accuracy of 88.24%, followed by CNN (88.14%) and ResNet (82.63%). These results demonstrate that deep convolutional architectures are effective in facial-based age estimation. This study contributes a comparative evaluation framework and highlights the importance of data diversity and model selection in deep learning-based age prediction.