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

Tony Gwyn, Kaushik Roy and Mustafa Atay
Additional contact information
Tony Gwyn: Department of Computer Science, North Carolina A&T State University, Greensboro, NC 27411, USA
Kaushik Roy: Department of Computer Science, North Carolina A&T State University, Greensboro, NC 27411, USA
Mustafa Atay: Department of Computer Science, Winston-Salem State University, Winston-Salem, NC 27110, USA

Future Internet, 2021, vol. 13, issue 7, 1-15

Abstract: 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.

Keywords: Convolutional Neural Networks; authentication; biometrics; face biometrics; facial recognition; classification methods (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1999-5903/13/7/164/pdf (application/pdf)
https://www.mdpi.com/1999-5903/13/7/164/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:13:y:2021:i:7:p:164-:d:582419

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().