Preprocessing of Dataset and Developing Smart Attendance Model Using Face Recognition

Preprocessing of Dataset and Developing Smart Attendance Model Using Face Recognition
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© 2024 by IJETT Journal
Volume-72 Issue-5
Year of Publication : 2024
Author : Aldinata, Haryono Soeparno
DOI : 10.14445/22315381/IJETT-V72I5P129

How to Cite?

Aldinata, Haryono Soeparno, "Preprocessing of Dataset and Developing Smart Attendance Model Using Face Recognition," International Journal of Engineering Trends and Technology, vol. 72, no. 5, pp. 282-289, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I5P129

Abstract
Attendance is one of the most widely used methods to keep track of discipline. Currently, manual attendance is in the form of RFID scans or students taking attendance from applications, with lecturers or academic persons to verify the attendance again. A smart attendance model can be used to take attendance automatically with the help of face recognition and can assist students who do not have face recognition algorithms capable able to doing such with lesser assistance. This study designs a model for smart attendance while performing the best preprocessing for both training and testing datasets to help improve the efficiency, accuracy, and effectiveness of the model. In the presented smart attendance model, training images consisting of the faces of each person will first be detected and be augmented for lighting variations before converted to face encodings and then be stored with their corresponding labels. In the testing phase, a video will be taken, in which a frame will be used as the input images to be then preprocessed to achieve the most faces possible. This is achievable by normalizing the brightness image resizing, and then face features will be extracted in the form of face encodings; before then, it will be compared to the face encodings extracted from the training phase. The results will then be divided into 3 classification tiers of accuracy, need further checking, and unrecognized face. From the experiment, the results show around 80% accurate faces, with around 20% needing further checking, and no unrecognized faces.

Keywords
Preprocessing, Dataset, Smart Attendance, Model, Face Recognition.

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