## 4 Selection of Effective Neural Network Architectures for Separate Modules
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multicolumn{1}{c}{} & \multicolumn{10}{c}{**Number of the Task**} \\  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10.1** & **10.2** \\ \hline MTCNN & + & & & & & 5 & & & & & \\ RetinaFace & + & & & & & 5 & & & & & \\ BlazeFace & + & & & & & 5 & & & & & \\ U-Net &! & &! &! & & & & & & & \\ DeepPixBiS & & + & +/- & & & & + & & & & \\ FaceNet & & & & & & & & & & + \\ Face Mesh & & & & & +/- & 468 & & & & & \\ OpenFace & & & & & & 68 & + & & & + \\ Face Alignment & & & & & & 68 & & & & & \\ Network & & & &! &! & & &! & & + \\ MobileNet & & & & & & & & & &! \\ Siamese Network & & & & & & & & & &! \\ Two\_channel & & & & & & & & & &! \\ Networks & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Matrix of the correspondence of neural network architectures to the tasks of the modules in the modular neural network model. the limited capabilities of this network to detect spoofing attacks based on artifacts in the external environment, which relate to objects to be recorded during face video registration. Also, this mark refers to the possibility of using the Face Mesh network to highlight IE contours and indicates that although this network does not, strictly speaking, highlight the contour, it defines the key points that relate to the IE. The numbers in the column for task number 6 indicate the number of key points determined on the face when using the corresponding NNM. The mark \"1\" means that the corresponding software is not freely available, but the availability of open source software, proven effectiveness in the relevant areas, and the availability of representative data sets ensure the possibility of implementing its corresponding development. At the same time, in the process of calculations, it was determined that when tasks 1, 3, 5, 6, 10.1, and 10.2 are solved, it is possible to apply several alternative variants of neural network architectures. For example, to solve task 1 (distinguishing FI boundaries) it is possible to use the neural network architectures MTCNN, RetinaFace, or BlazeFace, instrumental support for which is freely available. Moreover, to solve this problem, it is also possible to use the U-Net architecture, which provides an opportunity to increase the accuracy of the selection of FI boundaries by selecting the natural contours of the FI instead of defining a rectangular area that contains the FI, which is characteristic of MTCNN, RetinaFace, and BlazeFace. The same features apply to task 10.1 (recognition of a person based on image comparison), which is especially relevant in the case that the list of legitimate representatives of the CIF personnel needs to be promptly updated. In the process of calculations, it was determined that to solve this problem, it is advisable to use the NNMs FaceNet, Siamese Network, or Two_channel Network, which have approximately the same values for the efficiency function [16; 21]. Although the use of FaceNet is ensured by available tools, entering data on a new person into such an NNM necessitates the implementation of a rather complex and unstable setting procedure. For example, the effectiveness of FaceNet is significantly reduced with an imbalance in the FI data set, which can occur when the list of legitimate representatives of the CIF personnel is changed. Therefore, a solution to problem 10.1 was proposed by developing an appropriate module based on NNMs of the Siamese Network and Two_channel Network types, adapted to the comparison of images associated with FIs. The structure of the authors' version of the Siamese Network is shown in Figure 2a, and the structure of the authors' version of the Two_channel Networks is shown in Figure 2b. Note that in the authors' version, a classical CNN with four layers of convolution and three layers of pooling is used as the basis of individual branches of the Siamese Network.