Facial Recognition Using a Lightweight Deep Neural Networks

Abstract

Current facial recognition systems are still far away from the capability of the human’s face perception. Facial recognition systems can continue to be improved as technology evolves. The task of face recognition has been heavily explored in recent years. In this research, we provide our initial idea in developing Lightweight Deep Neural Networks for facial recognition. Although our goal was to create an optimal model that would exceed current facial recognition model performance, we could experiment and discover alternative approaches to multi-class facial recognition/classification. We tested with a dataset of 2800 images of men and women with specified image sizes. We created three CNN with various architectures, which we used to train with the chosen dataset for 20, 50, 100, and 200 classes per model. The experimental results exhibit the challenges of increasing the complexity of neural networks. From these results, we concluded that a Light CNN Model with a small number of layers had an average test accuracy of 94.19%, which was the best classification performance on unseen data.