ML-Based Face Attendance

October 2022—October 2022

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This project implements a machine learning-powered face recognition attendance system tailored for environments like classrooms or secure workplaces. Leveraging image processing techniques and a KNN classifier, the system can detect, identify, and verify individuals in real-time through webcam input. It minimizes the chances of proxy attendance by matching facial embeddings against stored datasets with high precision. All records are logged into a structured CSV file, making it simple to audit, export, or integrate with other systems. The solution prioritizes speed, accuracy, and user privacy, making it a practical tool for modern digital attendance tracking.