## 5 Experimental Evaluation of Neural Network Architectures of Individual Modules  
Using the data in Table 1, research was conducted aimed at clarifying the prospects of using the neural networks presented to solve the corresponding tasks implemented by the corresponding modules under various conditions of operation at the CIF. For this, two series of experiments were conducted. In the first series, the declared capabilities of  
Figure 2: Structures of the authors’ variants of neural network models designed for face recognition based on the comparison of two FIs. (**a**) Siamese Network, (**b**) Two_channel Network. neural networks marked with the symbol \"+\" in Table 1 were clarified. Attention was focused on the study of the influence of occlusions in the solution of tasks 1, 5, and 6, that is, the selection of FI and RO boundaries and the determination of the coordinates of control and key points. In the second series of experiments, the possibilities of the authors' implementation of neural networks marked with the symbol \"!\" were tested. The progress of the first series of experiments is illustrated with the help of Figures 3 and 4, which show the results of applying the Face Mesh network to plot key points on the selected FI from a photo card using the BlazeFace network. Thus, Figure 3 shows the results of drawing key points on a woman's face, generated using the hispersondoesnoteexist service ([[https://thispersondoesnoteexist.com/](https://thispersondoesnoteexist.com/)]([https://thispersondoesnoteexist.com/](https://thispersondoesnoteexist.com/))) (accessed on 3 November 2024), registered without the influence of interference (Figure 3a) and in the presence of transparent glasses on her face (Figure 3b). Figure 4 shows the results of drawing key points on an animated face registered without the influence of obstacles (Figure 4a) and in cases where the face has certain obstacles. In Figure 4b, the interference is a medical mask; in Figure 4c, it is dark glasses; and in Figure 4d, the key points are shown in the case of combined interference consisting of a medical mask and dark glasses. The results of the first series of experiments showed the possibility of effectively applying the MTCNN, RetinaFace, and BlazeFace networks to highlight in the frames of the video stream a rectangular area that corresponds to the face of a person regardless of their gender and race, both in the absence of obstacles during video registration and in cases with the presence of typical obstacles covering about 60% of the face area. At the same time, the following results are observed:  
Figure 4: Applying key points to an animated face. (**a**) No obstacles on faces, (**b**) obstacle—medical mask, (**c**) obstacle—dark glasses, (**d**) combined obstacle. Figure 3: Drawing key points on a woman’s face. (**a**) Key points on a woman’s face, (**b**) key points on the face of a woman in transparent glasses. * The BlazeFace network provides, with moderate accuracy, the highest-speed code and the possibility of deployment on devices with limited computing power. * The MTCNN network, due to its cascade structure, is the most accurate when it is necessary to highlight faces in images with different sizes and different MTCNN resolutions. However, the cascade structure of the MTCNN causes it to have a relatively low speed, which complicates the operational analysis of the video stream. Moreover, its low selection accuracy in the case that the size of the face occupies less than 10% of the image should be noted. * The RetinaFace network provides the highest accuracy in selecting faces fixed at different angles. Compared to the MTCNN, it has a higher speed, but it requires hardware with high computing power. * When choosing a network, the size of the input field should be taken into account; this is which is 320 \(\times\) 240 or 640 \(\times\) 480 pixels for MTCNN, 640 \(\times\) 640 or 800 \(\times\) 800 pixels for RetinaFace, and 128 \(\times\) 128 or 256 \(\times\) 256 pixels for BlazeFace.