Smart Attendence System Using Facial Recognition for Staff using AI/ML

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Published: 2026-03-13
DOI: https://doi.org/10.15662/IJRPETM.2026.0902005
Keywords:
Real-Time Tracking, Attendance Automation, Biometric Identification, Computer Vision, Deep Learning, Facial Recognition, Convolutional Neural Networks

Main Article Content

T.Pavan kumar, T.Abhishek goud, S.Yogesh, V.Manikanta, P.Dinesh
UG Student, Department of Computer Science and Engineering, Holy Mary Institute of Technology & Science, Telangana, India
B.Srinu
Asst. Professor, Department of Computer Science and Engineering, Holy Mary Institute of Technology & Science, Telangana, India
Dr. Prasad Dharnasi
Professor, Department of Computer Science and Engineering, Holy Mary Institute of Technology & Science, Telangana, India

Abstract

One of the practical consequences brought by the Covid-19 pandemic is the growing necessity for automated systems in order to enhance security, efficacy, and transparency in the management system of attendance in educational institutions, which is still in the rudimentary stages of development. While attendance systems that still use attendance books and fingerprint systems, are effortless to use, there is a lengthy risk of absenteeism, hygiene risks and a constant cost of maintenance. Specifically, this system is designed to recognize faces. Attendance is recorded by automatically recognizing faces, recognizing and recording attendance by way of neural networks. I. In Real-time face recognition, a model of 128-dimensional facial embedding is trained and tested on a database made of 3,000 photo images of 75 individuals and all of the people in the divergent, poses, and conditions, and face detection is performed using the Haar Cascade face recognition system. Further, we will recognize the face by using a pre-processing step that analyzes the recognition and similarity of the face using Euclidean distance. As such, my research concludes that the system achieves a recognition rate of 97.84 percent, a precision rate 97.65 percent, a recall rate of 97.21 percent, and an F1 score of 97.43 percent. Its false acceptance rate is 1.8 percent; its false rejection rate is 2.3 percent, which makes its FFRR, and inflexible and appropriate for use in institutions. Within the frame of the smart campus paradigm, this developed model provides attendance system automation, and contact-less, attendance systems, and systems which is secure, is a system in an educational institution.

Article Details

Issue

Vol. 9 No. 2 (2026): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

Smart Attendence System Using Facial Recognition for Staff using AI/ML. (2026). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(2), 513-519. https://doi.org/10.15662/IJRPETM.2026.0902005

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