This research presents a face recognition-based smart attendance system leveraging single image processing for real-timeattendance marking. Traditional attendance methods are inefficient, error-prone, and susceptible to manipulation. The proposedsystem employs machine learning algorithms such as Local Binary Patterns (LBP), Fisherfaces, and Eigenfaces to enhance accuracyand reliability. Designed for educational and corporate environments, the system eliminates the need for expensive hardware,reduces operational costs, and simplifies usage by relying solely on a standard camera.Extensive testing on a diverse dataset demonstrated a 96.7% recognition accuracy and an average processing time of 2.3 seconds perimage, ensuring suitability for real-time applications. Despite challenges like lighting variability and occlusions, the system’sscalability, security, and user-friendliness make it a robust solution. Future enhancements include integrating deep learning modelsand cloud-based analytics to further optimize performance and adaptability..Keywords: Face Recognition, Attendance System, LBP, Single Image Processing, Automated Attendancc