English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Online information systems currently heavily rely on the username andpassword traditional method for protecting information and controlling access.With the advancement in biometric technology and popularity of fields like AIand Machine Learning, biometric security is becoming increasingly popularbecause of the usability advantage. This paper reports how machine learningbased face recognition can be integrated into a web-based system as a method ofauthentication to reap the benefits of improved usability. This paper includesa comparison of combinations of detection and classification algorithms withFaceNet for face recognition. The results show that a combination of MTCNN fordetection, Facenet for generating embeddings, and LinearSVC for classificationoutperforms other combinations with a 95% accuracy. The resulting classifier isintegrated into the web-based system and used for authenticating users.

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