Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study
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 secu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/13/7/164 |
| _version_ | 1827688715694112768 |
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| author | Tony Gwyn Kaushik Roy Mustafa Atay |
| author_facet | Tony Gwyn Kaushik Roy Mustafa Atay |
| author_sort | Tony Gwyn |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:02:35Z |
| format | Article |
| id | doaj.art-50bbaf9aaefb434fbbec9f95fb6fb5c5 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T10:02:35Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-50bbaf9aaefb434fbbec9f95fb6fb5c52023-11-22T01:47:12ZengMDPI AGFuture Internet1999-59032021-06-0113716410.3390/fi13070164Face Recognition Using Popular Deep Net Architectures: A Brief Comparative StudyTony Gwyn0Kaushik Roy1Mustafa Atay2Department of Computer Science, North Carolina A&T State University, Greensboro, NC 27411, USADepartment of Computer Science, North Carolina A&T State University, Greensboro, NC 27411, USADepartment of Computer Science, Winston-Salem State University, Winston-Salem, NC 27110, USAIn 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.https://www.mdpi.com/1999-5903/13/7/164Convolutional Neural Networksauthenticationbiometricsface biometricsfacial recognitionclassification methods |
| spellingShingle | Tony Gwyn Kaushik Roy Mustafa Atay Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study Future Internet Convolutional Neural Networks authentication biometrics face biometrics facial recognition classification methods |
| title | Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study |
| title_full | Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study |
| title_fullStr | Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study |
| title_full_unstemmed | Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study |
| title_short | Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study |
| title_sort | face recognition using popular deep net architectures a brief comparative study |
| topic | Convolutional Neural Networks authentication biometrics face biometrics facial recognition classification methods |
| url | https://www.mdpi.com/1999-5903/13/7/164 |
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