# Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems
## 3 Verification System
### Selecting the Similarity Threshold
The results are recorded in Table 3.  
The results of the second test (Table 3) confirm that the optimal similarity threshold is 85%. At this threshold, User No. 4 was successfully recognized even at a similarity threshold of 95%. However, User No. 6 experienced difficulty with recognition at a threshold 10% lower. A significant factor affecting these results is the variation in conditions under which reference photos were captured. For best performance, the database should be created in consistent environmental conditions and with a uniform face position (preferably \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Set Similarity** & **User No. 1** & **User No. 2** & **User No. 3** & **User No. 4** & **User No. 5** & **User No. 6** \\
**Threshold** & **([PERSON])** & **([PERSON])** & **([PERSON])** & **([PERSON])** & **([PERSON])** & **([PERSON])** \\
**[\%]** & **[s]** & **[s]** & **[s]** & **[s]** & **[s]** & **[s]** \\ \hline
0 & \(\sim\)2 & \(\sim\)2 & \(\sim\)2 & \(\sim\)2 & \(\sim\)2 & \(\sim\)2 \\
10 & \(\sim\)2 & \(\sim\)2 & \(\sim\)2 & \(\sim\)2 & \(\sim\)2 & \(\sim\)2 \\
20 & \(\sim\)2 & \(\sim\)2 & \(\sim\)2 & \(\sim\)2 & \(\sim\)2 & \(\sim\)2 \\
30 & \(\sim\)2 & \(\sim\)2 & \(\sim\)2 & \(\sim\)2 & \(\sim\)2 & \(\sim\)2 \\
40 & \(\sim\)2 & \(\sim\)6 & \(\sim\)4 & \(\sim\)2 & \(\sim\)7 & \(\sim\)8 \\
50 & \(\sim\)2 & \(\sim\)36 & \(\sim\)10 & \(\sim\)3 & \(\sim\)28 & \(\sim\)35 \\
60 & \(\sim\)3 & unknown & \(\sim\)32 & \(\sim\)3 & \(\sim\)114 & unknown \\
70 & \(\sim\)4 & unknown & \(\sim\)74 & \(\sim\)4 & unknown & unknown \\
75 & \(\sim\)4 & unknown & unknown & \(\sim\)17 & unknown & unknown \\
80 & \(\sim\)9 & unknown & unknown & \(\sim\)34 & unknown & unknown \\
85 & \(\sim\)16 & unknown & unknown & \(\sim\)96 & unknown & unknown \\
90 & \(\sim\)29 & unknown & unknown & unknown & unknown & unknown \\ \hline \end{tabular}
\end{table} 
Table 2: Face recognition time results (first test—Table 1) with only User No. 1 introduced into the system and varying similarity thresholds. frontal). However, achieving identical conditions for all reference images is challenging. Therefore, setting the similarity threshold at 85% provides a balance between reliable recognition accuracy and relatively short identification times. For applications like access control systems, accuracy is paramount due to the potential handling of sensitive data. As such, the selected threshold must ensure both dependable operation and practical usability. In the next test, the similarity threshold was set at 85%. All test users were introduced into the system, and face recognition was conducted under varying environmental conditions and with different face positions. The results are presented in Table 4.  
The results confirm that standardizing conditions for capturing test images is essential to ensure system accuracy. During the identification process, users should face the camera directly and avoid moving their heads until recognition is complete. Proper lighting conditions are crucial; face recognition should not be performed in poorly lit areas, and overly bright lighting should also be avoided as it can negatively impact results. The final test of the similarity threshold setting validated the system's performance at an 85% threshold value. For this test, each user was added to the database and subjected to three identification attempts under consistent conditions: a room with artificial lighting and users positioned frontally to the camera. The duration of the identification process  
Figure 12: Graph of recognition time dependency on similarity threshold value.