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.
ABSTRACT
This paper presents the research and development of information technology for analysis and classification of chest X-ray images in order to automatically detect the signs of the disease, specifically pneumonia, what is the most relevant in the conditions of COVID-19 pandemic. Information technology is based on the developed mathematical model through complex training of neural networks. The dataset used for the experimental studies and neural networks training consisted of 35,000 images ranging in size from 200×200 px to 2500×2500 px. Convolutional neural networks were used to fulfill the goal of software creation based on developed information technology. As a result of experiments, the weighted average value of F1 metric of 97.05% was obtained, that is close to the recognition rate of a physician. During the research the decision support software based on developed information technology was created with an aim to assist the physician in making a decision, help in the analysis of lungs X-rays for pneumonia, and also allow to store all the necessary information about the patients in one repository. The program was developed using Microsoft technologies, including the C# programming language and a technology environment designed to develop a user interface - WPF. Also, software was implemented using the MVVM architecture and ML.NET as a tool for implementation of a neural network. The Nvidia RTX 2070 Super graphics processor (GPU) and CUDA technology were used to train the neural network. Created software based on developed information technology for chest X-ray images analysis allows to record patients, classify and process images, add confirmations of physicians, and can be used as an accessory instrument to diagnose pneumonia, which will reduce the strain on the radiologist and allow to process larger number of X-rays images more effective. © 2022 Copyright for this paper by its authors.
ABSTRACT
This paper describes the research of information technology for person identification by occluded face image. As far as the spread of coronavirus disease (COVID-19) raised the problem of identification by facial image with masks covering the face as a prevention measure, the research of face recognition and identification technologies has become crucial for all of the cybersecurity areas based on the identity verification by digital technologies. The proposed algorithm, that is the cornerstone of the information technology, is based on the methods of anisotropic diffusion for image preprocessing, Gabor wavelet transform, histogram of oriented gradients (HOG) and local binary patterns in 1-dimensional space (1DLBP) for image feature vector extraction, and square Euclidean distance metric for vector classification. Experiments on the proposed technology after applying it on the occluded images from the SCface database provided the result of 85%, increased on 2.5% after image format and resolution conversion. © 2022 IEEE.