Mfrd-80k: A dataset and benchmark for masked face recognition

Wearing face masks in public spaces has become an essential step to prevent the spread of COVID-19. This step poses some challenges to conventional face recognition due to several reasons: 1) the absence of large real-world masked face recognition dataset, and 2) the loss of some visual cues due to the occlusion by the face masks. To address these challenges, this paper presents a real-world masked face recognition dataset that consists of 80500 masked face images of 161 subjects, referred to as MFRD-80K dataset. Every subject contributes 500 masked face images, which are then partitioned into 60:20:20 for train, validation and test. Subsequently, we conduct some benchmark studies to evaluate the performance of the existing face recognition and classification methods on the MFRD-80K dataset. The methods include k-Nearest Neighbour, Multinomial Logistic Regression, Support Vector Machines, Random Forest, Multilayer Perceptron and Convolutional Neural Networks. Since the parameter settings affect the performance of each method, a grid search is performed to determine the optimal parameter settings. The empirical results demonstrate that Convolutional Neural Network achieves the highest test accuracy of 97.16% on MFRD-80K dataset.