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

• URL: http://arxiv.org/abs/2103.15144v1
• Date: Sun, 28 Mar 2021 14:49:17 GMT
• Title: Face Recognition as a Method of Authentication in a Web-Based System
• Authors: Ben Wycliff Mugalu, Rodrick Calvin Wamala, Jonathan Serugunda, Andrew Katumba
• Abstract summary: 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.
• Score: 1.0323063834827415
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Multi-modal biometric authentication: Leveraging shared layer architectures for enhanced security [0.0]
  We introduce a novel multi-modal biometric authentication system that integrates facial, vocal, and signature data to enhance security measures. Our model architecture incorporates dual shared layers alongside modality-specific enhancements for comprehensive feature extraction. Our approach demonstrates significant improvements in authentication accuracy and robustness, paving the way for advanced secure identity verification solutions.
  arXiv  Detail & Related papers  (2024-11-04T14:27:10Z)
• Time-Aware Face Anti-Spoofing with Rotation Invariant Local Binary Patterns and Deep Learning [50.79277723970418]
  imitation attacks can lead to erroneous identification and subsequent authentication of attackers. Similar to face recognition, imitation attacks can also be detected with Machine Learning. We propose a novel approach that promises high classification accuracy by combining previously unused features with time-aware deep learning strategies.
  arXiv  Detail & Related papers  (2024-08-27T07:26:10Z)
• Biometrics Employing Neural Network [0.0]
  Fingerprints, iris and retina patterns, facial recognition, hand shapes, palm prints, and voice recognition are frequently used forms of biometrics. For systems to be effective and widely accepted, the error rate in recognition and verification must approach zero. Artificial Neural Networks, which simulate the human brain's operations, present themselves as a promising approach.
  arXiv  Detail & Related papers  (2024-02-01T03:59:04Z)
• Agile gesture recognition for capacitive sensing devices: adapting on-the-job [55.40855017016652]
  We demonstrate a hand gesture recognition system that uses signals from capacitive sensors embedded into the etee hand controller. The controller generates real-time signals from each of the wearer five fingers. We use a machine learning technique to analyse the time series signals and identify three features that can represent 5 fingers within 500 ms.
  arXiv  Detail & Related papers  (2023-05-12T17:24:02Z)
• Analysis of Recent Trends in Face Recognition Systems [0.0]
  Due to inter-class similarities and intra-class variations, face recognition systems generate false match and false non-match errors respectively. Recent research focuses on improving the robustness of extracted features and the pre-processing algorithms to enhance recognition accuracy.
  arXiv  Detail & Related papers  (2023-04-23T18:55:45Z)
• Face Presentation Attack Detection [59.05779913403134]
  Face recognition technology has been widely used in daily interactive applications such as checking-in and mobile payment. However, its vulnerability to presentation attacks (PAs) limits its reliable use in ultra-secure applicational scenarios.
  arXiv  Detail & Related papers  (2022-12-07T14:51:17Z)
• PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition [55.858374644761525]
  Face recognition networks encode information about sensitive attributes while being trained for identity classification. Existing bias mitigation approaches require end-to-end training and are unable to achieve high verification accuracy. We present a descriptors-based adversarial de-biasing approach called Protected Attribute Suppression System ( PASS)' Pass can be trained on top of descriptors obtained from any previously trained high-performing network to classify identities and simultaneously reduce encoding of sensitive attributes.
  arXiv  Detail & Related papers  (2021-08-09T00:39:22Z)
• AuthNet: A Deep Learning based Authentication Mechanism using Temporal Facial Feature Movements [0.0]
  We propose an alternative authentication mechanism that uses both facial recognition and the unique movements of that particular face while uttering a password. The proposed model is not inhibited by language barriers because a user can set a password in any language.
  arXiv  Detail & Related papers  (2020-12-04T10:46:12Z)
• Face recognition using PCA integrated with Delaunay triangulation [0.0]
  The research examines the integration of Principal Component Analysis with Delaunay Triangulation. The method triangulates a set of face landmark points and obtains eigenfaces of the provided images. It compares the algorithm with traditional PCA and discusses the inclusion of different face landmark points to deliver an effective recognition rate.
  arXiv  Detail & Related papers  (2020-11-25T14:46:08Z)
• Face Anti-Spoofing Via Disentangled Representation Learning [90.90512800361742]
  Face anti-spoofing is crucial to security of face recognition systems. We propose a novel perspective of face anti-spoofing that disentangles the liveness features and content features from images.
  arXiv  Detail & Related papers  (2020-08-19T03:54:23Z)
• Identity-Aware Attribute Recognition via Real-Time Distributed Inference in Mobile Edge Clouds [53.07042574352251]
  We design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework.
  arXiv  Detail & Related papers  (2020-08-12T12:03:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.