Face Recognition as a Method of Authentication in a Web-Based System
This addresses authentication usability for web system users, but it is incremental as it applies existing methods to a new application.
The paper tackled integrating machine learning-based face recognition into a web-based authentication system to improve usability, achieving 95% accuracy with a combination of MTCNN, FaceNet, and LinearSVC.
Online information systems currently heavily rely on the username and password traditional method for protecting information and controlling access. With the advancement in biometric technology and popularity of fields like AI and Machine Learning, biometric security is becoming increasingly popular because of the usability advantage. This paper reports how machine learning based face recognition can be integrated into a web-based system as a method of authentication to reap the benefits of improved usability. This paper includes a comparison of combinations of detection and classification algorithms with FaceNet for face recognition. The results show that a combination of MTCNN for detection, Facenet for generating embeddings, and LinearSVC for classification outperforms other combinations with a 95% accuracy. The resulting classifier is integrated into the web-based system and used for authenticating users.