Automatic Attendance System based on FaceRecognition using Machine Learning

Sharmila, S.; Nagasai, G. Kavya; Sowmya, M.; Prasanna, A.Sai; Sri, S. Navya; Meghana, N.

doi:10.1109/iccmc56507.2023.10084017

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…al. [1] compared the results of multiple machine learning methods for Automatic attendance system. This research work presents the working of a cascade classifier built with machine learning to improve the face detection results.…”

Section: Literature Reviewmentioning

confidence: 99%

Face Recognition Attendance System

koli

2024

IJSREM

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Attendance taking is one of a tedious and important tasks in colleges, universities, organizations, schools, and offices, which must be done on a daily basis. The majority of the time, it is done manually, such as by calling by name or by roll number. The main goal of this project is to create a Face Recognition-based attendance system that will turn this manual process into an automated one. This project meets the requirements for bringing modernization to the way attendance is handled, as well as the criteria for time management. This device is installed in the classroom, where and student's information, such as name, roll number, class, sec, and photographs, is trained. The images are extracted using Open CV. Before the start of the corresponding class, the student can approach the machine, which will begin taking pictures and comparing them to the qualified dataset. Logitech C270 web camera and NVIDIA Jetson Nano Developer kit were used in this project as the camera and processing board. The image is processed as follows: first, faces are identified using a Haarcascade classifier, then faces are recognized using the LBPH (Local Binary Pattern Histogram) Algorithm, histogram data is checked against an established dataset, and the device automatically labels attendance. An Excel sheet is developed, and it is updated every hour with the information from the respective class instructor.

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Section: Literature Reviewmentioning

confidence: 99%