Detection of Face Mask in Thermal Images Using Deep CNN

Sandhya, B.; Sesidhar, D. V. S. R.; Reddy, Lathasree; Meghana, T.; Sony, B.

doi:10.1007/978-981-16-9705-0_15

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…A similar classification problem was described in [17]. The model based on MobileNetV2 was used for feature extraction from a thermal image and for detecting if a person is wearing a mask.…”

Section: Introductionmentioning

confidence: 99%

Mask Detection and Classification in Thermal Face Images

Kowalczyk¹,

Sobotka²,

Rumiński³

2023

IEEE Access

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Face masks are recommended to reduce the transmission of many viruses, especially SARS-CoV-2. Therefore, the automatic detection of whether there is a mask on the face, what type of mask is worn, and how it is worn is an important research topic. In this work, the use of thermal imaging was considered to analyze the possibility of detecting (localizing) a mask on the face, as well as to check whether it is possible to classify the type of mask on the face. The previously proposed dataset of thermal images was extended and annotated with the description of a type of mask and a location of a mask within a face. Different deep learning models were adapted. The best model for face mask detection turned out to be the Yolov5 model in the ''nano'' version, reaching mAP higher than 97% and precision of about 95%. High accuracy was also obtained for mask type classification. The best results were obtained for the convolutional neural network model built on an autoencoder initially trained in the thermal image reconstruction problem. The pretrained encoder was used to train a classifier which achieved an accuracy of 91%.INDEX TERMS Deep neural networks, epidemic prevention, health infrastructure, mask area detection, mask type classification, thermal imaging.

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Section: Introductionmentioning

confidence: 99%