## 3 Materials and Methods  
From a technical point of view, the present authentication mechanism used for experimentation comprises two main physical components: a Raspberry Pi 4 Model B system with an 8 GB LPDDR4 SDRAM and 1.5 GHz quad-core processor, interconnected with an Arduino ESP32 microcontroller, powerful tools of the last generation that integrate Wi-Fi and Bluetooth wireless capabilities for connections, as displayed in Figure 1. The Raspberry Pi microprocessor acts as an active hotspot that can be accessed by other devices and establishes a connection with the Arduino ESP 32 development board and the PC via Wi-Fi, which are considered to be Wi-Fi clients. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Authentication Scheme** & **Element Description** & \begin{tabular}{c} **Encryption** \\ **Time** \\ \end{tabular} & \
\begin{tabular}{c} **Distribution** \\ **Implementation** \\ \end{tabular} &
\begin{tabular}{c} **Level of** \\ **Accuracy** \\ \end{tabular} \\ \hline Proposed system & Face/BCV & 1 s & Client/Server & Hardware & 96.80\% \\ [PERSON] et al. [11] & Face dataset/CKKS SEAL & 1.712 s & Client/Server/Cloud & Software & 96.71\% \\ [PERSON] et al. [13] & Iris dataset/BFV & 1.1938 s & Client/Server/Cloud & Software & 98.12\% \\ [PERSON] and [PERSON] [41] & Face dataset/BFV & 0.53 s & Client/Server/Cloud & Software & N/A \\ [PERSON] [45] & Face dataset/BFV & 280.19 ms & Client & Software & 96.74\% \\ [PERSON] et al. [46] & Face dataset/BFV & 2.5 ms & Client/Server & Software & 95\% \\ [PERSON] et al. [48] & Face dataset/CKKS & 7 ms & Client/Server & Software & 95\% \\ [PERSON] and [PERSON] [49] & Face dataset/BCV & 49.5 ms & Client & Software & N/A \\ [PERSON] and [PERSON] [49] & Face/RSA/Paillier/Chaos & 1.9870 s & Client & Software & N/A \\ [PERSON] and [PERSON] [50] & Face dataset/TFHE & 456 s & Client/Server & Software & N/A \\ [PERSON] et al. [44] & Face/CKKS HEAN & 2.83 ms & Client & Software & 96.10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the proposed model with existing authentication models. Considering the process description, the proposed authentication mechanism is a complex system that implies several configurations using different software environments, namely:  
* The configuration of the Arduino ESP 32 development board with the open-source software Arduino Integrated Development Environment (IDE), version 1.8.19 and its additional libraries
* The COM port is unlocked using the C++ environment in Dev-C++, version 5.11 to realize the connection between the application running on Raspberry Pi and the specified serial port of the PC to read and report the collected data. * The installation and configuration of the Raspberry Pi 4 with Python software, version 3.10, 64-bit and its standard libraries for image processing OpenCV 4.9.0.80, FaceRecognition 1.3.0 were used for identification and authentication. More than that, an advanced configuration in the Windows registry key and group policy management is needed to have sufficient rights to execute facial recognition and distinguish individuals based on their physical features. In addition, a secure graphical web interface in PHP and Apache is created for the users' database management, which can be accessed online using a link associated with a static IP address, as shown in Figure 2.  
During the authentication process, Raspberry Pi 4 scans the user faces found in the frame and compares them with those in the biometric database; if one face is identified, it provides the login information to ESP32 via Wi-Fi. Raspberry acts as a hotspot and ESP32 as a Wi-Fi client, as presented in the picture above. This mechanism comprises two main stages. The first stage consists of creating a database with user accounts and their pictures centralized on the external device, whereas the second stage includes the processes of identification, authentication, and authorization of users in the IT system.