Emerging Trends in Face Recognition Technologies and Attendance Systems: A Review

The integration of face recognition technology into real-time attendance systems has largely revolutionized traditional attendance management methods. These systems can utilize advanced methods to ensure accurate, efficient, and automated attendance tracking, offering numerous advantages over manual or RFID-based methods. This paper will provide a detailed review over the current state of face recognition technologies, including the evolution of facial recognition technologies, including the evolution of facial recognition algorithms, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and cutting-edge deep learning models like Convolutional Neural Networks (CNNs). Further, this paper highlights key technologies enabling real-time implementations, such as GPU acceleration, the use of large datasets, and edge computing. With this many challenges will be explored which will be associated with these systems, including environmental variability, security concerns, computational scalability, and also demographic biases. Finally, recent advancements like 3D facial recognition, adverbial learning, and lightweight models for edge devices can be discussed as potential solutions to some of the challenges, with these research directions for the future, ethical frameworks are included, Internet of Things (IoT) technologies can also be integrated, multimodal biometrics and federated learning. This review aims to guide future development and deployment and development of real-time face recognition systems for attendance amendment in diverse applications.