GMPPS Based Face Mask Detection of Infrared Thermal Image for Ultra-Small Training Data Set

Tian, Jiang

doi:10.1109/icdsca53499.2021.9650310

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…In the article [18], face detection was performed based on features extracted by Max-pooling and fast PCA, and SVM was used to classify these features. The authors relied on a small dataset (containing only 800 images), and the average face mask recognition proposed by the method can be up to over 99.6%.…”

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%