Study of input images parameters for improving person identification information technology

Abstract

This article describes the research of the algorithm based on local-textural descriptors, which is the basis of the information technology of person identification, with the aim of increasing its efficiency and reducing the probability of identification errors. An analysis of previous studies has shown that the performance of a face recognition and identification algorithm can vary significantly after applying it to images from different datasets. In addition, the issue of using local texture descriptors in the tasks of face recognition and identification on images of different quality has not been sufficiently studied in modern research. Therefore, the main goal of this work is to study the parameters of face images that will be used for identification process with the appliance of the algorithm based on such local-textural methods as histograms of oriented gradients and local binary patterns in one-dimensional space. During the experiments, it was established that the algorithm identification accuracy rate can be decreased by 35-70% when it is applied to image samples taken in unconstrained conditions. In order to investigate the possibility of reducing the variation of the results, experiments were conducted on the same images with the transformation of their properties, such as format and resolution. During the research, experiments were carried out with several of the most common databases of face images, as a result of which the efficiency of the algorithm reached the level of accuracy of identification of 95% on images taken under controlled conditions and unified within one database. As a result, it was found that in some cases, the conversion of the image format submitted to the input of the algorithm can increase the accuracy of the algorithm’s identification by 5%. Also, the efficiency of the algorithm is affected by the resolution of the input images, in particular, due to the application of the algorithm to transformed images from the Database of Faces database, the accuracy of identification increased by 5-10%, FERET — decreased by 5-35%, SCface — decreased by 10-30%, CFP — increased by 5-10%, Tinyface — increased by 25-35%, LFW — increased by 15%, AgeDB — increased by 5%. The highest identification accuracy rate of the algorithm, which is 95%, was obtained after its application to images taken under constrained conditions and unified within the same database.