The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIV-2/W1-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-2/W1-2021, 177–182, 2021
https://doi.org/10.5194/isprs-archives-XLIV-2-W1-2021-177-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-2/W1-2021, 177–182, 2021
https://doi.org/10.5194/isprs-archives-XLIV-2-W1-2021-177-2021

  15 Apr 2021

15 Apr 2021

A NOVEL METHOD FOR PROTECTIVE FACE MASK DETECTION USING CONVOLUTIONAL NEURAL NETWORKS AND IMAGE HISTOGRAMS

E. Ryumina, D. Ryumin, D. Ivanko, and A. Karpov E. Ryumina et al.
  • St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg, Russian Federation

Keywords: Computer Vision, Object Detection, Mask Detection, Face in a Medical Mask, CNN, 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.