## 2 Related Work
It is noted that face recognition devices built on the Face ID technology are adapted to obstacles related to the application of makeup, the appearance of hair, and the presence of a hat, scarf, transparent glasses, contact lenses, or a medical mask. In the latter case, a necessary condition for authentication is the video registration of the eyes and the activation of the \"Face ID with a mask\" mode. Also, these tools can be used in various lighting conditions. A significant deterioration in accuracy is noted in the presence of dark sunglasses. In addition, the use of Face ID technology allows one to recognize whether the user's eyes are open, which is used to change the settings of the device's screen. The recommended distance from the face to the video camera is 25-50 cm. In the process of setting up Face ID, the user's face is scanned several times in different angles, which ensures the creation of a 3D face template, which is also used to confirm \"survivability\" for the recognition of spoofing attacks. Unlike Face ID, tools on the Android platform provide video registration of the face only in the visible light range. At the same time, the recognition of spoofing attacks is realized due to the detection of reflections characteristic of a glass or paper surface, the determination of the boundaries of the device used to demonstrate a fake image, the analysis of image artifacts (moire, defocus, distortion) and the presence of facial micromimicry. A decrease in the functionality of recognition tools is noted when the level of illumination is reduced. Thus, the analysis of well-known applied BA systems according to FIs and IEs shows that, in general, the prospects for their improvement are related to ensuring the sufficient accuracy of person recognition, taking into account the possibility of implementing spoofing attacks in conditions of uneven lighting and the influence of interference. Since the results of [12; 13; 14] indicate that the basis of such systems are neural network tools, in the next stage, scientific and applied works devoted to such tools were analyzed. According to data from literature reviews [6; 11; 15], a large number of studies devoted to the application of NNMs for person recognition based on FIs and IEs exist today. At the same time, several different approaches to building a network can be distinguished. In works [16; 17], an NNM is used to compare the input image with only one image from the data set that meets the FI and IE standards of legitimate users. In this case, the authentication process involves a sequential comparison of the input image with all images associated with each of the users. In [18], it is proposed to use an NNM trained on examples of images that correspond to all legitimate users. That is, such an NNM solves the problem of assigning the input image to one of the predefined classes of legitimate users. Although the recognition accuracy of such a model is considered somewhat high, its serious drawback is the difficulty of entering data for a new user due to the need to completely retrain the model. At the same time, the authentication procedure can be correlated, in the first case, with the solution of the image classification problem and, in the second case, with the solution of the image comparison problem. Another approach, described in [19], involves the use of an additional neural network module designed for pre-processing images before they are submitted to the recognition module. An approach to neural network face recognition based on the integrated analysis of FIs and IEs is also known [20]. Another aspect of the development of neural network recognition tools was studied in works [21; 22], where it was proposed to implement neural network analysis of images in a video stream taking into account information from several frames. At the same time, in [21], it is proposed to carry out recognition based on a frame-by-frame comparison of the incoming video stream with a reference image.