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Article

Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study

1
Department of Computer Science, North Carolina A&T State University, Greensboro, NC 27411, USA
2
Department of Computer Science, Winston-Salem State University, Winston-Salem, NC 27110, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Steven Furnell
Future Internet 2021, 13(7), 164; https://doi.org/10.3390/fi13070164
Received: 1 June 2021 / Revised: 22 June 2021 / Accepted: 22 June 2021 / Published: 25 June 2021
(This article belongs to the Special Issue Machine Learning Approaches for User Identity)
In the realm of computer security, the username/password standard is becoming increasingly antiquated. Usage of the same username and password across various accounts can leave a user open to potential vulnerabilities. Authentication methods of the future need to maintain the ability to provide secure access without a reduction in speed. Facial recognition technologies are quickly becoming integral parts of user security, allowing for a secondary level of user authentication. Augmenting traditional username and password security with facial biometrics has already seen impressive results; however, studying these techniques is necessary to determine how effective these methods are within various parameters. A Convolutional Neural Network (CNN) is a powerful classification approach which is often used for image identification and verification. Quite recently, CNNs have shown great promise in the area of facial image recognition. The comparative study proposed in this paper offers an in-depth analysis of several state-of-the-art deep learning based-facial recognition technologies, to determine via accuracy and other metrics which of those are most effective. In our study, VGG-16 and VGG-19 showed the highest levels of image recognition accuracy, as well as F1-Score. The most favorable configurations of CNN should be documented as an effective way to potentially augment the current username/password standard by increasing the current method’s security with additional facial biometrics. View Full-Text
Keywords: Convolutional Neural Networks; authentication; biometrics; face biometrics; facial recognition; classification methods Convolutional Neural Networks; authentication; biometrics; face biometrics; facial recognition; classification methods
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MDPI and ACS Style

Gwyn, T.; Roy, K.; Atay, M. Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study. Future Internet 2021, 13, 164. https://doi.org/10.3390/fi13070164

AMA Style

Gwyn T, Roy K, Atay M. Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study. Future Internet. 2021; 13(7):164. https://doi.org/10.3390/fi13070164

Chicago/Turabian Style

Gwyn, Tony, Kaushik Roy, and Mustafa Atay. 2021. "Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study" Future Internet 13, no. 7: 164. https://doi.org/10.3390/fi13070164

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