Senior Design Project Facial Recognition Using Machine Learning

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dc.contributor.advisorDr. Mohammad Ashrafuzzaman Khan
dc.contributor.authorTabassum Mahbub Liya
dc.contributor.authorMofijul Alam Ovi
dc.contributor.id1812474042
dc.contributor.id1821202042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2024-05-05
dc.date.accessioned2024-05-05T06:44:09Z
dc.date.available2024-05-05T06:44:09Z
dc.date.issued2022
dc.description.abstractNowadays facial recognition strategy is a widely used method for authentication in the fast-growing digital world. Due to the advancement of machine learning algorithms, facial recognition has become the most effective, trusted, and more popular than fingerprint or any other authentication method. It is now also widely used for video surveillance or detecting any specific actions. Therefore, the industry is heavily investing more and more funds in this technology. Besides that, face recognition has a wide range of applications in fields such as intelligent security and access control, biometrics, safeguarding, verification, attendance accounting, and machine vision, among others. Face recognition has a lot of advantages over other technologies for determining a person’s personality: there is no need to physically meet the person, which is the most appropriate method for mass applications, and there is no need for specialized or expensive equipment. The difficulty of recognizing and identifying a person’s face using convolutional neural networks that process frames from a camera in real time or from a recorded video file, followed by the entry of the recognized individual into a database is discussed in this article. The Multitasking Cascade Convolutional Neural Network (MTCNN) is made up of three convolutional networks (P-Net, R-Net, and O-Net) that can surpass numerous face identification tests while preserving real-time performance. The proposed method for human face recognition was implemented as a software product, tested, and shown to have a 96.02% chance of being correct in real time.
dc.description.degreeUndergraduate
dc.identifier.cd600000037
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/580
dc.language.isoen_US
dc.publisherNorth South University
dc.rights© NSU Library
dc.subjectTECHNOLOGY::Electrical engineering, electronics and photonics::Electrical engineering
dc.titleSenior Design Project Facial Recognition Using Machine Learning
dc.typeProject
oaire.citation.endPage60
oaire.citation.startPage1
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