doi: 10.17586/2226-1494-2022-22-3-415-432


Methods for audiovisual recognition of people in masks 

K. E. Kosulin, A. A. Karpov


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Article in Russian

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Kosulin K.E., Karpov A.A. Methods for audiovisual recognition of people in masks. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2022, vol. 22, no. 3, pp. 415–432 (in Russian). doi: 10.17586/2226-1494-2022-22-3-415-432


Abstract
In the modern world, wearing masks, respirators and facial clothes is very popular. The novel coronavirus pandemic that began in 2019 has also significantly increased the applicability of masks in public places. The most affective person recognition methods are identification by face image and voice recording. However, person recognition systems are facing new challenges due to masks covering most of the subject’s face. Existence of new problems for intelligent systems determines the relevance of masked person recognition systems research, therefore the subject of the study is the systems and datasets for masked people recognition. The article discusses analysis of the main approaches to masked people identity recognition: masked face recognition, masked voice recognition and audiovisual methods. In addition, this article includes comparative analysis of images and recordings datasets required for person recognition systems. The results of the study showed that among the methods that use face images the most effective are methods based on convolutional neural networks and the mask area feature extraction. The methods of x-vector analysis showed a slight drop in efficiency which allows us to conclude that they are applicable in the tasks of recognizing the identity of a speaker in a mask. Results of this study help with formulation of requirements for perspective masked person recognition systems and determining directions for further research.

Keywords: person recognition, facial biometrics, voice biometrics, medical masks, personal protective equipment, audiovisual features, information fusion

Acknowledgements. This work was partially supported by the RFBR (project No. 20-04-60529), by the Council for Grants of the President of Russia (grant No. NSH-17.2022.1.6), as well as by the Russian state research (No. 0073-2019-0005).

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