A novel method for protective face mask detection using convolutional neural networks and image histograms

Abstract

This paper proposes a new hybrid method for automatic detection and recognition of the presence/absence of a protective mask on human's face. It combines visual features extracted using Convolutional Neural Network (CNN) with image histograms that convey information about pixel intensity. Several pre-trained models for building feature extraction systems using a CNN and several types of image histograms are considered in this paper. We test our approach on the Medical Mask Dataset and perform cross-corpus analysis on two other databases named Masked Faces (MAFA) and Real-World Masked Face Dataset (RMFD). We demonstrate that the proposed hybrid method increases the Unweighted Average Recalls (UARs) of recognition of the presence/absence of a protective mask on human's face in comparison with traditional CNNs on the MAFA and RMFD databases by 0.96% and 1.32%, respectively. The proposed method can be generalized and used for other tasks of biometry, computer vision, machine learning and automatic face recognition.