Citation
How, Jun Jie and Tan, Shing Chiang and Liew, Kim Soon (2022) Masked Face Recognition Attendance System Using A Modified Convolutional Neural Network. International Journal on Robotics, Automation and Sciences, 4. pp. 23-29. ISSN 2682-860X Full text not available from this repository.
Official URL: https://doi.org/10.33093/ijoras.2022.4.4
Abstract
n this paper, a masked face recognition based attendance system is developed by modifying aversion of convolutional neural network (CNN).In this regard, a Support Vector Machine is integrated in the CNN to replace its original Softmax classifier to perform the task.T he performance of the modified CNN in recognizing masked faces in a 5-fold cross validation was compared that of other CNNs. The experimental results show high effectiveness of the proposed CNN (i.e. 98.92%) in recognizing masked faces for recording attendance.
Item Type: | Article |
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Uncontrolled Keywords: | Deep learning, convolutional neural network, support vector machine, masked face images |
Subjects: | Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75-76.95 Calculating machines |
Divisions: | Faculty of Information Science and Technology (FIST) |
Depositing User: | Ms Nurul Iqtiani Ahmad |
Date Deposited: | 22 Jul 2022 01:06 |
Last Modified: | 22 Jul 2022 01:06 |
URII: | http://shdl.mmu.edu.my/id/eprint/10168 |
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