Knock Knock, Who’s There: Facial Recognition using CNN-based Classifiers

Qiyu Sun, Alexander Redei

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)9-16
Number of pages8
JournalInternational Journal of Advanced Computer Science and Applications
Issue number1
StatePublished - 2022


  • Convolutional neural networks
  • Deep learning
  • Deepface
  • Face recognition


Dive into the research topics of 'Knock Knock, Who’s There: Facial Recognition using CNN-based Classifiers'. Together they form a unique fingerprint.

Cite this