# Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems
## 1 Introduction
Our testing encompassed diverse environmental conditions, facial expressions, and user demographics, achieving an average recognition time of 10.5 s. While the system demonstrated reliable performance, challenges such asvulnerabilities to biometric similarities and spoofing highlight the need for additional security measures, such as integrating thermal imaging. This paper discusses the development, testing, and potential applications of the system, emphasizing its strengths and limitations. This study is critical as it explores the application of low-cost, accessible hardware like the Raspberry Pi in facial recognition systems, addressing real-world concerns such as privacy, bias, and performance under variable conditions. Mainstream solutions often rely on expensive hardware or cloud processing, which raises privacy concerns and limits accessibility. This research contributes by demonstrating how a compact and affordable device can deliver effective results while maintaining privacy through local processing. Unlike studies that depend on high-end hardware or cloud-based solutions, this research emphasizes leveraging the Raspberry Pi--a low-power, cost-effective platform--to make facial recognition more accessible. Many existing studies utilize cloud processing, which raises data security and privacy concerns. In contrast, this study ensures that all data are processed locally on the Raspberry Pi, reducing vulnerabilities associated with external data handling. It uniquely addresses variable conditions such as lighting changes, facial angles, and occlusions, testing the robustness of the system in scenarios that mimic real-life applications. By incorporating diverse datasets, this research explicitly tackles algorithmic bias and discrimination, an aspect often overlooked in other studies, particularly those focused on advanced hardware solutions. The strengths of this approach are evident in several aspects. The use of the Raspberry Pi makes this system affordable and widely accessible, lowering the barrier to implementing facial recognition in various fields. Local data processing enhances privacy, making it suitable for applications in sensitive areas such as home security and personal automation. Additionally, the modularity of the Raspberry Pi allows for incremental upgrades, such as adding GPU acceleration or integrating higher-resolution cameras, enabling the system to scale with user needs. Testing under variable conditions ensures reliability in real-world scenarios, adding credibility to the system's practical utility. Nevertheless, some weaknesses reveal themselves in computational limitations. While the Raspberry Pi 3 provides sufficient power for basic applications, it struggles with the real-time processing of large datasets or multiple faces without optimization or external hardware support. Algorithmic constraints are apparent as the system relies on basic models like Haar cascades, which are less effective for extreme facial angles or complex scenarios compared to advanced deep learning-based approaches. Although efforts were made to include diverse datasets, the relatively small scale of testing (e.g., six participants for threshold determination) may limit the generalizability of the findings. The performance under challenging conditions still requires extended research. Variable conditions such as poor lighting or severe occlusions remain challenging, necessitating further enhancement of hardware or algorithmic robustness. Future work includes exploring higher-resolution cameras and expanding the user database to improve the system's robustness and applicability in real-world scenarios. This paper is structured as follows: Section 2 explains the methodology, focusing on the implementation of the Haar cascade classifier and the AdaBoost algorithm on a Raspberry Pi microcomputer. It covers the system architecture, the training process, and the selection of the similarity threshold.