A Quantitative Study on the FaceNet System

Rajesh Gopakumar*, Karunakar A. Kotegar, M. Vishal Anand

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Face recognition is the science of identifying and recognizing human faces in various situations, keeping the constraints like pose variation and occlusion in mind. Due to its impactful applications in safety and security systems, face recognition is becoming extremely popular and is researched extensively even today. The FaceNet method is among the most tested approaches in Deep learning face identification. This method uses a deep convolution neural network for training the data. The face embedding generated can be used to train a face identification system. This study aims to comprehend the FaceNet system, evaluate its performance, and test its accuracy on seven standard datasets. The study also tries to compare how well the FaceNet method works compared to other popular holistic and hybrid methods. From the outcomes of this study, we can conclude that FaceNet showed outstanding results and was better than the other methods. The FaceNet system reached a minimum of 90% accuracy on all standard datasets used on both the pre-trained models, which is a significant number for any face recognition method.

Original languageEnglish
Title of host publicationAdvanced Computational and Communication Paradigms - Proceedings of ICACCP 2023
EditorsSamarjeet Borah, Tapan K. Gandhi, Vincenzo Piuri
PublisherSpringer Science and Business Media Deutschland GmbH
Pages211-223
Number of pages13
ISBN (Print)9789819942831
DOIs
Publication statusPublished - 2023
Event4th International Conference on Advanced Computational and Communication Paradigms, ICACCP 2023 - Sikkim, India
Duration: 16-02-202318-02-2023

Publication series

NameLecture Notes in Networks and Systems
Volume535 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference4th International Conference on Advanced Computational and Communication Paradigms, ICACCP 2023
Country/TerritoryIndia
CitySikkim
Period16-02-2318-02-23

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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