# Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems
## 1 Introduction  
Facial recognition technology has experienced remarkable advancements in recent years, unlocking diverse applications across fields ranging from security to consumer electronics. The origins of facial recognition research date back to the 1960s, when early methods relied on analyzing facial proportions and features [1]. By the 1990s, more advanced techniques, such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), emerged, enabling more effective matching of facial images to stored patterns in databases [2,3]. The 21 st century saw the rise of sophisticated methods, including local binary pattern histograms (LBPHs) and Fisherfaces, further enhancing the accuracy and robustness of facial recognition systems [4]. The significance of facial recognition extends beyond convenience, offering secure, user-friendly solutions across various domains. Biometric systems have become integral to smartphones for secure unlocking and to banking systems for transaction authentication, eliminating the reliance on traditional methods like passwords and PINs [5]. Public security and surveillance extensively leverage this technology for monitoring public areas and airports, aiding in identifying persons of interest [6, 7, 8]. Commercial applications include customer behavior analysis in e-commerce, personalized advertising, and enhanced customer service [9, 10, 11]. In banking and finance, facial recognition systems support ATMs and remote client authentication, boosting transaction security [12, 13, 14]. Face recognition techniques have evolved through three primary approaches:  
* Geometric Methods: These early techniques analyzed the geometry of facial features, such as distances between the eyes, nose, and mouth. While straightforward, they struggled with variations in lighting, angles, and facial expressions [15, 16, 17]. * Feature-Based Methods: Techniques like LBPH and Gabor filters analyze textures and structures on the face's surface, offering greater resilience to changes in appearance and diversity of facial features [18, 19, 20, 21, 22, 23]. * Deep Learning Methods: With advancements in neural networks, particularly Convolutional Neural Networks (CNNs), facial recognition achieved unprecedented precision [24, 25, 26, 27, 28, 29, 30]. Models like DeepFace, FaceNet, and OpenFace excel in recognizing faces even in large datasets and challenging conditions, such as low light, varied expressions, and partial occlusions. Despite its benefits, facial recognition technology faces critical challenges. Privacy concerns, particularly in public surveillance, raise ethical questions about its misuse by corporations and governments [8, 31, 32, 33, 34, 35]. Algorithmic bias and discrimination are also significant issues, as studies reveal disparities in recognition accuracy based on skin color, gender, and ethnicity. Efforts by major companies to address these biases underline the importance of fairness in such systems [36, 37, 38, 39, 40]. Additionally, variable conditions, such as lighting, occlusions (e.g., masks), and appearance changes (e.g., makeup, glasses), continue to affect recognition accuracy [41, 42, 43, 44]. Current research aims to address these challenges by improving accuracy, minimizing algorithmic bias, and expanding applications [45, 46]. For instance, emotion recognition and mental state analysis have found utility in fields such as healthcare, human resources, and marketing [36, 47, 48, 49, 50]. The future of facial recognition systems hinges on social acceptance, ethical considerations, and legal frameworks to protect user privacy and ensure equitable use. In this study, we focus on developing a facial recognition system that balances accuracy, efficiency, and affordability. Utilizing the Haar cascade classifier for rapid feature detection and the AdaBoost algorithm for enhanced precision, we implemented the system on a Raspberry Pi microcomputer [51, 52, 53, 54, 55].