Classifying Age and Gender on Historical Photographs using Convolutional Neural Networks

Abstract: This project intends to classify faces in historical photographs into age and gender. The goal was to demonstrate an algorithm specialized on classifying historical images, as well as an interface where users can insert pictures for analysis. This project aims to facilitate historical research by contributing with new tools for image analysis. The algorithm is developed in the programming language Python and uses Convolution Neural Networks (CNN) to classify age and gender. The user interface is developed in the JavaScript framework React.js and communicates with the Python algorithm via a Node.js server. The main results are that the gender classification algorithm has an accuracy of 96\% and the age detection algorithm has a mean age error of 4.3 years. The results also indicated that our algorithms perform better on historical images than commonly used state-of-the-art classification models.