Citation

Yo, Ming Chun and Chong, Siew Chin and Chong, Lee Ying (2025) Sparse CNN: leveraging deep learning and sparse representation for masked face recognition. International Journal of Information Technology. ISSN 2511-2104

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Abstract

Due to the COVID-19 pandemic, face masks’ widespread adoption in public has spurred an urgent need to investigate the efficacy of masked face recognition systems. In this research, a new approach of leveraging sparse representation and deep learning techniques, termed Sparse Representation based Classification incorporated with tailored CNN (Sparse CNN) is proposed to enhance the performance of masked face recognition. This method utilises Convolutional Neural Network (CNN) as feature extractor and integrates Sparse Representation based Classification (SRC) as a classifier to improve the accuracy of the masked face recognition systems. With the combination of both these techniques, the proposed Sparse CNN leverages the stability against noise and high accuracy at recognising interference issues of sparse representation while utilizing CNN’s shift invariant property to minimise alignment issues for creating a more robust and reliable masked face recognition system. The experimental results prove that Sparse CNN complements each other and achieves accuracies of 88.89% and 93.41% on Labelled Faces in the Wild Stimulated Masked Face Recognition Dataset (LFW-SMFRD) and Real-World Masked Face Recognition Dataset (RMFRD) respectively.

Item Type:	Article
Uncontrolled Keywords:	Convolutional neural network, deep learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	05 Mar 2025 07:10
Last Modified:	05 Mar 2025 07:10
URII:	http://shdl.mmu.edu.my/id/eprint/13562

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