Citation

Chong, Lucas Wei Jie and Chong, Siew Chin and Ong, Thian Song (2023) Masked Face Recognition Using Histogram-Based Recurrent Neural Network. Journal of Imaging, 9 (2). p. 38. ISSN 2313-433X

Text jimaging-09-00038.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Masked face recognition (MFR) is an interesting topic in which researchers have tried to find a better solution to improve and enhance performance. Recently, COVID-19 caused most of the recognition system fails to recognize facial images since the current face recognition cannot accurately capture or detect masked face images. This paper introduces the proposed method known as histogram-based recurrent neural network (HRNN) MFR to solve the undetected masked face problem. The proposed method includes the feature descriptor of histograms of oriented gradients (HOG) as the feature extraction process and recurrent neural network (RNN) as the deep learning process. We have proven that the combination of both approaches works well and achieves a high true acceptance rate (TAR) of 99 percent. In addition, the proposed method is designed to overcome the underfitting problem and reduce computational burdens with large-scale dataset training. The experiments were conducted on two benchmark datasets which are RMFD (Real-World Masked Face Dataset) and Labeled Face in the Wild Simulated Masked Face Dataset (LFW-SMFD) to vindicate the viability of the proposed HRNN method.

Item Type:	Article
Uncontrolled Keywords:	masked face recognition; neural network; histogram of gradients; deep learning; recurrent
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:	11 Apr 2023 02:00
Last Modified:	11 Apr 2023 02:00
URII:	http://shdl.mmu.edu.my/id/eprint/11322

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