Open AccessFace Recognition Service Model for Student Identity Verification Using Deep Neural Network and Support Vector Machine (SVM)
Author(s) -
Ngonadi I. Vivian,
Orobor Anderson Ise
Publication year - 2020
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit2063225
Subject(s) - support vector machine , computer science , usable , artificial neural network , context (archaeology) , identity (music) , identification (biology) , artificial intelligence , machine learning , service (business) , mobile phone , face (sociological concept) , computer security , data mining , world wide web , telecommunications , paleontology , social science , physics , botany , economy , sociology , acoustics , economics , biology
Impersonation in the context of examination, is a situation where a candidate sits in an examination for another candidate pretending to the real candidate. In many institutions in Nigeria, to mitigate this act, students are expected to present a means of identification before entering the examination hall. However, this approach is not sufficient to determine the eligibility of a student for an examination as these means of identification can easily be falsified. This paper therefore, develops a face recognition web service model for student identity verification using Deep Neural Network (DNN) and Support Vector Machine (SVM). The aim is to mitigate examination impersonation by simple face scan using mobile phone and also to make such a model accessible and re-usable for seamless integration with any kind of student identity verification project.