# Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems
## 3 Verification System
### Performance and Effectiveness Test of the Face Recognition System  
The test was conducted in accordance with the established procedures for capturing reference and test images. The application's similarity threshold was set at a constant value of 85% for the data matrix. The test was performed under consistent environmental conditions, with all users registered in the database. Unlike previous tests, recognition attempts lasting longer than 30 s were classified as negative results. The outcomes are presented in Table 7.  
The results confirm the system's accuracy regardless of variations in user age and gender. The average recognition time (Figure 14) is 10.583 s, which is considered satisfactory. Following the procedures outlined in the previous subsection, the system is suitable for critical applications, such as controlling access to rooms containing sensitive data. However, it is recommended to implement a maximum identification time of 30 s in the program as an additional safeguard against incorrect recognition results, complementing the similarity threshold. While this limit enhances reliability, it could lead to user frustration in cases of identification failure within the allotted time, even if the user is registered in the system. Such failures could stem from non-compliance with image-capture procedures or changes in daily appearance, such as different makeup or facial hair growth. Maintaining consistent environmental conditions in an industrial face identification process is highly challenging. Additionally, biometric facial similarities among individuals can complicate the development of an infallible face recognition system. Future research could explore the impact of facial aging on the identification process, helping to determine when users should update their reference images in the database to minimize the risk of non-recognition despite prior verification. Another important consideration is the system's performance with a significantly larger database, such as one containing over a thousand users. This scenario would likely increase recognition times and require greater computational power. Despite these challenges, the system has demonstrated effectiveness and efficiency for a small group of users (tested with 12 individuals) when proper verification procedures are followed. A final test assessed the program's resilience against spoofing by substituting user photos for live camera input. This test evaluated the system's ability to detect and prevent fraudulent attempts. The test was conducted with the same group of 12 users, using ID card photos to simulate frontal face positioning relative to the camera lens. The results are summarized in Table 8. \begin{table}
\begin{tabular}{l c c} \hline \hline
**User** & **Recognition Result** & **Recognition Time [s]** \\ \hline User No. 1 ([PERSON]—25 years old) & [PERSON] & \(\sim\)11 \\ User No. 2 ([PERSON]—29 years old) & [PERSON] & \(\sim\)10 \\ User No. 3 ([PERSON]—43 years old) & [PERSON] & \(\sim\)9 \\ User No. 4 ([PERSON]—49 years old) & [PERSON] & \(\sim\)12 \\ User No. 5 ([PERSON]—35 years old) & [PERSON] & \(\sim\)11 \\ User No. 6 ([PERSON]—39 years old) & [PERSON] & \(\sim\)9 \\ User No. 7 ([PERSON]—27 years old) & [PERSON] & \(\sim\)12 \\ User No. 8 ([PERSON]—16 years old) & [PERSON] & \(\sim\)8 \\ User No. 9 ([PERSON]—19 years old) & [PERSON] & \(\sim\)11 \\ User No. 10 ([PERSON]—51 years old) & [PERSON] & \(\sim\)10 \\ User No. 11 ([PERSON]—46 years old) & [PERSON] & \(\sim\)10 \\ User No. 12 ([PERSON]—22 years old) & [PERSON] & \(\sim\)14 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Face recognition system effectiveness results. The results confirm the ease of tricking the system, which eliminates it as a security system for so-called \"hard\" processes, where the possibility of error is unacceptable. Recognition times for printed photos were comparable to those for live user identification.