Gender-specific Facial Age Group Classification Using Deep Learning

Abstract

Facial age is one of the prominent features needed to make decisions, such as accessing certain areas or resources, targeted advertising, or more straightforward decisions such as addressing one another. In machine learning, facial age estimation is a typical facial analysis subtask in which a model learns the different facial ageing features from several facial images. Despite several studies confirming a relationship between age and gender, very few studies explored the idea of introducing a gender-based system that consists of two separate models, each trained on a specific gender group. This study attempts to bridge this gap by introducing an age estimation system that consists of two main components. The first component is a custom-built gender classifier that distinguishes females and males apart. The second is an age estimation module that consists of two models. Model A is trained only on female images, while model B is trained only on male images. The system takes an input image, extracts the facial gender then passes the image to the appropriate model based on the predicted gender label. Our age estimation models are based on the Visual Geometry Group (VGG16) networks and have been modified to fit the nature of our problem. The models produce accuracies of more than 85% individually, and the system achieves an overall accuracy of 80%. The proposed system is trained and tested on the UTKFace dataset and cross-validated on the FG-NET dataset to validate the performance on unseen data.