# Face Recognition as a Method of Authentication in a Web-Based System

arXiv: https://arxiv.org/abs/2103.15144 · HTML: https://mlsanity.com/paper/2103.15144/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 28 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)

## Built on by (most cited first, 1 of 1)

- [IAP: Invisible Adversarial Patch Attack through Perceptibility-Aware Localization and Perturbation Optimization](https://mlsanity.com/paper/2507.06856/) (2025)

## Abstract (arXiv)

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.