Masked face recognition with principal random forest convolutional neural network (PRFCNN)


Chong, Lucas Wei Jie and Chong, Siew Chin and Ong, Thian Song (2022) Masked face recognition with principal random forest convolutional neural network (PRFCNN). Journal of Intelligent & Fuzzy Systems, 43 (6). pp. 8371-8383. ISSN 1064-1246

Full text not available from this repository.


Masked face recognition embarks the interest among the researchers to find a better algorithm to improve the performance of face recognition applications, especially in the Covid-19 pandemic lately. This paper introduces a proposed masked face recognition method known as Principal Random Forest Convolutional Neural Network (PRFCNN). This method utilizes the strengths of Principal Component Analysis (PCA) with the combination of Random Forest algorithm in Convolution Neural Network to pre-train the masked face features. PRFCNN is designed to assist in extracting more salient features and prevent overfitting problems. Experiments are conducted on two benchmarked datasets, RMFD (Real-World Masked Face Dataset) and LFW Simulated Masked Face Dataset using various parameter settings. The experimental result with a minimum recognition rate of 90% accuracy promises the effectiveness of the proposed PRFCNN over the other state-of-the-art methods.

Item Type: Article
Uncontrolled Keywords: Covid-19, PRFCNN, random forest, principal component analysis, convolutional neural network
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 13 Jan 2023 01:41
Last Modified: 13 Jan 2023 01:41


Downloads per month over past year

View ItemEdit (login required)