Facial Age Estimation using Convolutional Neural Networks

TL;DR

This paper presents a deep convolutional neural network for facial age estimation trained on multiple datasets, achieving moderate accuracy and providing a web-based age estimation tool.

Contribution

The paper introduces a CNN architecture trained from scratch on combined datasets for age estimation and provides an accessible web application for real-time predictions.

Findings

Achieved 52% accuracy on test set

Achieved 30% exact accuracy on Adience benchmark

Developed a web-based age estimation script

Abstract

This paper is a part of a student project in Machine Learning at the Norwegian University of Science and Technology. In this paper, a deep convolutional neural network with five convolutional layers and three fully-connected layers is presented to estimate the ages of individuals based on images. The model is in its entirety trained from scratch, where a combination of three different datasets is used as training data. These datasets are the APPA dataset, UTK dataset, and the IMDB dataset. The images were preprocessed using a proprietary face-recognition software. Our model is evaluated on both a held-out test set, and on the Adience benchmark. On the test set, our model achieves a categorical accuracy of 52%. On the Adience benchmark, our model proves inferior compared with other leading models, with an exact accuray of 30%, and an one-off accuracy of 46%. Furthermore, a script was created, allowing users to estimate their age directly using their web camera. The script, alongside all other code, is located in our GitHub repository: AgeNet.