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

Article type: Research Article

Authors: Wei-Jie, Lucas Chong | Chong, Siew-Chin^{; *} | Ong, Thian-Song

Affiliations: Faculty of Information Science & Technology, Multimedia University, Malaysia

Correspondence: [*] Corresponding author: Siew-Chin Chong, Faculty of Information Science & Technology, Multimedia University, Malaysia. E-mail: [email protected].

Abstract: 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.

Keywords: Covid-19, PRFCNN, random forest, principal component analysis, convolutional neural network

DOI: 10.3233/JIFS-220667

Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8371-8383, 2022