Abstract: Artificial intelligence (AI) has captured the public’s imagination. Performance gains in computing hardware, and the ubiquity of data have enabled new innovations in the field. In 2014, Facebook’s DeepFace AI took the facial recognition industry by storm with its splendid performance on image recognition. While newer models exist, DeepFace was the first to achieve near-human level performance. To better understand how this breakthrough performance was achieved, we developed our own facial image detection models. In this paper, we developed and evaluated six Convolutional Neural Net (CNN) models inspired by the DeepFace architecture to explore facial feature identification. This research made use of the You Tube Faces (YTF) dataset which included 621,126 images consisting of 1,595 identities. Three models leveraged pretrained layers from VGG16 and InceptionResNetV2, whereas the other three did not. Our best model achieved a 84.6% accuracy on the test dataset.

Qiyu Sun and Alexander Redei, “Knock Knock, Who’s There: Facial Recognition using CNN-based Classifiers” International Journal of Advanced Computer Science and Applications(IJACSA), 13(1), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130102

@article{Sun2022,
title = {Knock Knock, Who’s There: Facial Recognition using CNN-based Classifiers},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130102},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130102},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {1},
author = {Qiyu Sun and Alexander Redei}
}