Open AccessFacial Recognition Using a Lightweight Deep Neural Networks
Author(s) -
Jonathan Hiebert,
Feezan Mazhar,
Micahl Derosa,
Alaa Sheta
Publication year - 2021
Publication title -
journal of advanced computer science and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-4332
DOI - 10.14419/jacst.v10i1.31632
Subject(s) - computer science , facial recognition system , artificial intelligence , pattern recognition (psychology) , artificial neural network , face (sociological concept) , convolutional neural network , task (project management) , deep learning , deep neural networks , machine learning , three dimensional face recognition , perception , speech recognition , face detection , social science , management , neuroscience , sociology , economics , biology
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.