# Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems
## 4 Conclusions  
The main goal of this project was to design a face detection and identification system capable of autonomously creating and utilizing a reference image database. This objective was successfully achieved, as the system demonstrated solid performance throughout testing. Built on a Raspberry Pi microcomputer, the application operates efficiently, achieving an average recognition time of 10.5 s. When standardized procedures for capturing reference and test images are followed under controlled environmental conditions, the system delivers near-flawless accuracy, as confirmed by the test results. Although the  
Figure 14: Sample tested images with varying facial expressions. Horizontal black line—the average recognition time 10.583 s. \begin{table}
\begin{tabular}{l c c} \hline \hline
**User** & **Recognition Result** & **Recognition Time [s]** \\ \hline User No. 1 ([PERSON]—25 years old) & [PERSON] & \textasci{}15 \\ User No. 2 ([PERSON]—29 years old) & unknown & - \\ User No. 3 ([PERSON]—43 years old) & [PERSON] & \textasci{}11 \\ User No. 4 ([PERSON]—49 years old) & [PERSON] & \textasci{}24 \\ User No. 5 ([PERSON]—35 years old) & [PERSON] & \textasci{}11 \\ User No. 6 ([PERSON]—39 years old) & unknown & - \\ User No. 7 ([PERSON]—27 years old) & [PERSON] & \textasci{}20 \\ User No. 8 ([PERSON]—16 years old) & [PERSON] & \textasci{}16 \\ User No. 9 ([PERSON]—19 years old) & [PERSON] & \textasci{}9 \\ User No. 10 ([PERSON]—51 years old) & [PERSON] & \textasci{}19 \\ User No. 11 ([PERSON]—46 years old) & unknown & - \\ User No. 12 ([PERSON]—22 years old) & [PERSON] & \textasci{}10 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: User identification results are based on their photo. system performs well for small user groups and controlled scenarios, there are several areas where further research is needed to improve its robustness and scalability:  
* Database Size and Biometric Variations: To assess scalability and reliability, the system needs to be tested with larger databases, exceeding a thousand users, including individuals with significant biometric similarities. * Hardware Impact: The current setup uses a standard Raspberry Pi-compatible camera module. Initial tests suggest that higher-resolution cameras could improve accuracy and facial feature detection, particularly when the reference images are of higher quality than the test images. Exploring the impact of hardware upgrades on system performance, especially under challenging conditions like poor lighting or occlusions, would be a valuable next step. * Algorithm and Library Performance: The system employs the Haar cascade classifier for face detection and data matrix generation, achieving high accuracy with frontal faces. However, its performance decreases with extreme facial angles. Leveraging additional tools from the OpenCV library, such as deep learning-based models, could mitigate these limitations and broaden the system's capabilities. * Security Enhancements: While the system works well for access control in environments dealing with sensitive data, it is vulnerable to spoofing via printed photographs. Adding features like thermal imaging to differentiate living faces from static images or incorporating additional biometric verifications (e.g., fingerprint or retina scans) would enhance security. * Beyond access control, the system's flexibility enables a range of potential applications:
* Retail and Surveillance: tracking customer movements or behavior in stores. * Data Collection: gathering pedestrian statistics or analyzing consumer habits. * Media Analysis: measuring screen time for actors in media productions. Although these extensions were beyond the original scope of the project, they highlight the system's adaptability for future development [69; 70]. Implementing such features would require additional modules, like object tracking, and further refinement of the algorithms. This system has proven to be a reliable facial recognition tool under specific conditions [71].