Attendance management plays a crucial role in educational institutions as it directly reflects student engagement and academic performance. However, as the number of students increases, traditional attendance methods become more time-consuming, prone to human errors, and susceptible to proxy attendance. To address these challenges, biometric identification systems have been introduced, starting with fingerprint-based systems and evolving into face recognition-based systems. While face recognition systems offer a more seamless and automated solution, they come with their own set of challenges. Factors such as variations in facial angles, lighting conditions, and camera positioning can impact detection accuracy. In a face recognition-based attendance system for educational institutes, the complexity lies in detecting and recognizing multiple faces simultaneously while ensuring high accuracy and minimizing false negatives. Although numerous pre-trained models are available for face detection and recognition, achieving a system with 100% accuracy remains a significant challenge. Despite these limitations, advancements in technology continue to improve the reliability and efficiency of face recognition-based attendance systems.