Mathematical Methods for Information Technology of Biometric Identification in Conditions of Incomplete Data

The purpose of this research is to develop mathematical methods for information technology of biometric identification which will allow to recognize person’s face in conditions of incomplete data such as wearing a medical face masks during the pandemic. During the ongoing pandemic researchers focus on quick and effective solutions to develop technologies that handle this problem. This research concentrates on the analysis of the already existing solutions and proposes a mathematical method of face identification for information technology based on wavelet transform under the condition of wearing masks by people. During this research, the experiments with face detection and recognition have been performed with the constraint information of covered face. There is no database of face images with masks, therefore a new database was created. This database contains 820 images of 40 people, whose faces was limited only by top part of the face (forehead, eyes). First experimental part was performed with the use of standard Python library face_recognition, which allows to perform face recognition from Python or from the command line with one of the simplest face recognition libraries. It built based on dlib face recognition toolkit with deep learning – it is a ResNet network that contains 29 conv layers. In the second set of experiments FaceNet system was used. It is a system that after high-quality features extracting from the face containing image creates a face embedding and predicts these features representation in a form of 128-element vector. Third part of experiments was performed to analyze the efficiency of three well-known face recognition methods: Eigenfaces, Fisherfaces and LBPH. Eigenfaces algorithm considers that different face parts are not identically significant in a face recognition process. It processes all the training images of all the people as a whole and extracts the components which are relevant and useful. Fisherfaces algorithm extracts principal components that differentiate one person from the others, so an individual's components become more useful over the others. The of LBPH algorithm is to find face local structure by comparing each pixel to its neighboring pixel, forming a list of local binary patterns, that can be converted into a decimal number. Mathematical methods proposed in this research based on wavelet transform, that is widely used in the image processing tasks. Wavelet transform provides processing of patterns hidden in the data performing data analysis in general as well as in the detail. To compare the results of the commonly used algorithms with wavelet transform there was developed algorithm with the use of Daubechies wavelets and reverse biorthogonal wavelets. The results of experimentation analysis indicate that the popular and commonly used methods of face identification do not demonstrate high efficiency results. Proposed mathematical methods for information technology based on wavelet transform improves the face recognition and identification process under the condition of faces covered with mask. Specifically, the most accurate identification rate of 77,5% was obtained with the use of Daubechies wavelets.