doi: 10.17586/2226-1494-2022-22-3-547-558


Method for generating masks on face images and systems for their recognition

G. A. Kukharev, E. V. Ryumina, N. A. Shulgin


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Article in Russian

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Kukharev G.A., Ryumina E.V., Shulgin N.A. Method for generating masks on face images and systems for their recognition. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2022, vol. 22, no. 3, pp. 547–558 (in Russian). doi: 10.17586/2226-1494-2022-22-3-547-558


Abstract
The problem of masked face recognition is investigated. It is shown that real masks of various shapes, textures and colors have become a problem for state-of-the-art face recognition systems. A reason for this is the lack of the necessary real training datasets. Creation of new data based on simple methods of forming masks on face images could solve this problem. An original method is proposed including the generation of various types, shapes, and colors of masks directly on the original texture of face images. The formation of the masks on the faces of individuals, on faces in group photos, and in scenes with streams of people was taken into account. Based on 100 original face images from the CUHK Face Sketch Database, a test database was created that includes more than 20,000 masked faces images which available for use. Experiments were carried out to recognize faces from the test database within the implemented four systems, among which three are state-of-the-art systems based on “deep learning” and one is deterministic system based on the cosine-transform. The performance of these systems was evaluated, the obtained results of masked face recognition were interpreted, and the masks that were a problem for selected four systems were noted. The proposed mask generation method can be used to create corpora and test databases of images with masks. The obtained results will be useful to researchers and specialists in the field of image processing and analysis.

Keywords: masks generation, face images recognition, facial anthropometry, neural networks, feature extractor, cosine similarity metric, minimum distance criterion

Acknowledgements. The work was supported by the RFBR project No. 20-04-60529-viruses, and also partially under the budget topic No. FFZF-2022-0005.

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