## 5 Experimental Evaluation of Neural Network Architectures of Individual Modules
At the same time, for the Siamese Network, the value of the loss indicator (corresponds to the degree of discrepancy between the NNM predictions and the actual data labels) is about 0.02, and the value of the precision indicator (the proportion of correct positive predictions among all predicted positive cases) and recall (the proportion of correct positive predictions among all real positive cases) are equal to 0.99. The Two_channel Network reaches approximately the same values for the accuracy, loss, precision, and recall parameters after 50 training epochs. Thus, the experimental studies confirmed the expediency of using the Siamese Network and Two_channel Network to determine the legitimacy of representatives of CIF personnel through identity recognition, which is implemented by comparing the registered FIs with the FIs of legitimate representatives. At the same time, a comparison of the dynamics of the learning parameters of the Siamese Network and Two_channel Network shows that the Two_channel Network is more effective from the point of view of learning efficiency. At the same time, the number of weighting factors in the Siamese Network is 38,964,545, and the number of weighting factors in the Two_channel Network is 2,570,849. That is, the computing resource capacity of the Two_channel Network is approximately 15 times smaller than the computing resource capacity of the Siamese Network, which is another advantage of the Two_channel Network. However, the advantages of the Siamese Network include the flexibility of the concept of its construction, which potentially allows for a significant increase in its efficiency due to the use of the most advanced neural network architectures adapted to FI analysis in the CNN block (Figure 2a). Figure 5: Variation in the accuracy indicator depending on the number of training epochs for the Siamese Network and Two_channel Network. **(a)** Siamese Network, **(b)** Two_channel Network. For example, in the CNN block, instead of the traditionally used LeNet-type architecture, it is possible to use a MobileNet-type architecture, which was confirmed with the help of experimental evaluation studies. Thus, if there is a need for operational deployment of the BA system, priority should be given to the Two_channel Network; however, in the absence of strict limitations regarding the time and resources available for development, it is advisable to carry out research aimed at the development of a modified Siamese Network. Experiments related to the study of U-Net and MobileNet networks showed the feasibility of their application in solving the corresponding tasks listed in Table 1. At the same time, in task 6 (determination of sets of coordinates of control and key points) under conditions of limited resources, priority should be given to a MobileNet network, and the application of U-Net can be correlated with the need to ensure the high accuracy of results. Also, when solving task 6, the advantages of tools based on U-Net and MobileNet relative to the available tools based on the MTCNN, RetinaFace, BlazeFace, Face Mesh, OpenFace, and the Face Alignment Network shown in Table 1 include the possibility of determining a set of key points corresponding to specific conditions of the given task. The expediency of using the U-Net network in solving task 1 (selecting FI boundaries) is explained by the possibility of accurately selecting natural FI contours instead of framing the FI using a rectangular frame, as is typical of the MTCNN, RetinaFace, and BlazeFace networks. To verify this statement, the U-Net network was used after being trained on the Human Face Segmentation data set, which is freely available at the link [[https://www.kaggle.com/datasets/dnayan/human-face-segmentation/data](https://www.kaggle.com/datasets/dnayan/human-face-segmentation/data)]([https://www.kaggle.com/datasets/dnayan/human-face-segmentation/data](https://www.kaggle.com/datasets/dnayan/human-face-segmentation/data)) (accessed on 3 November 2024).