Smart Management Attendance System with Facial Recognition Using Computer Vision Techniques on the Raspberry Pi

Fadhillah Azmi

doi:10.55524/ijircst.2023.11.1.9

Abstract

In this study, a smart attendance system was created using computer vision techniques embedded in the Raspberry Pi device. The initial process is carried out by recording students taking certain courses and taking facial images for the needs of the system database. In the next stage, the system will be regulated according to the time of lecture entry to determine which students will attend the lecture. Every student who wants to enter the classroom is identified by taking facial images with a camera from the Raspberry Pi device to identify and determine the time students enter to attend lectures. Each image taken will be processed to detect the presence of a face using the Viola-Jones method and to extract features using the LBP method to obtain the feature value of each image. The results obtained will be stored in the system for the facial recognition process. The final stage of the system being built is to perform face recognition according to the initial image to carry out the attendance process. This process will be carried out using the normalized cross-correlation (NCC) technique, in which the highest feature similarity obtained between the initial image and the newly captured image is the result of recognition by the system. From the trials that have been carried out, the developed system gives good results in obtaining attendance management in a fairly efficient manner, and the algorithm proposed for facial recognition obtains good results with an accuracy rate of 97.54%.

Keywords

Smart Attendance System, Face Recognition, Raspberry Pi, LBP, Normalized Cross Correlation

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Cites this article as

[Fadhillah Azmi, Amir Saleh, Achmad Ridwan (2023), Smart Management Attendance System with Facial Recognition Using Computer Vision Techniques on the Raspberry Pi , International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Vol-11, Issue-1, Page No-38-44], (ISSN 2347 - 5552). www.ijircst.org

Corresponding Author

Fadhillah Azmi

Department of Electrical Engineering, Universitas Medan Area, Medan, Indonesia

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