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Preprocessing Methods Study to Improve Information Technology for Person Identification by Occluded Image
2nd International Workshop on Information Technologies: Theoretical and Applied Problems, ITTAP 2022 ; 3309:66-76, 2022.
Article in English | Scopus | ID: covidwho-2167842
ABSTRACT
The coronavirus pandemic has become challenging issue for the face recognition and identification technologies. Most algorithms failed because of the presence of medical masks on the faces. Such an issue made it difficult for the decision-making systems to provide the correct results during the face recognition and person identification process. Although, for the past three years many of these problems have been overcome, the new adversary attacks arose, that allow to evade the identification systems. Therefore, the development of information technologies for person identification robust to the presence of occlusion on faces is still up to date. This paper describes the preprocessing methods study with an aim to improve performance of information technology of person identification by occluded face image. Information technology is based on the algorithm that consist of Gabor wavelet transformation as an image processing method for forming a global face image, local binary patterns in one-dimensional space and a histogram of oriented gradients for forming a vector of image features, Euclidean squared distance metric for vector classification. For the purpose of information technology improvement, the experimental research was conducted with the use of variety of preprocessing

methods:

anisotropic diffusion, image histogram equalization and both of these methods applied. During the research there were used The Database of Faces database, the FERET database and the SCface database. Images from these databases were processed in order to consider it occluded and converted to uncompressed and compressed formats to conduct the experiments more clearly. The results of the experiments have shown that preprocessing by anisotropic diffusion and image histogram equalization along with conversion to uncompressed format can increase the accuracy of the algorithm performance on 5-7.5% in some cases. Also, the usage of image histogram equalization by itself on the images converted to compressed format can increase the identification accuracy rate of the algorithm on 2.5%. © 2022 Copyright for this paper by its authors.
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Collection: Databases of international organizations Database: Scopus Language: English Journal: 2nd International Workshop on Information Technologies: Theoretical and Applied Problems, ITTAP 2022 Year: 2022 Document Type: Article

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Collection: Databases of international organizations Database: Scopus Language: English Journal: 2nd International Workshop on Information Technologies: Theoretical and Applied Problems, ITTAP 2022 Year: 2022 Document Type: Article