AI-Powered Facial Recognition Attendance System Using Deep Learning and Computer Vision

Traditional attendance methods like manual entry and RFID-based systems are slow, errorprone, and vulnerable to manipulation, creating the need for a more secure and efficient solution. To address these issues, an AI-powered Automated Attendance Management System (AAMS) is proposed, integrating computer vision and machine learning techniques for realtime face detection and recognition. Developed using Python, the system leverages OpenCV for image preprocessing, while SQLite and MySQL are used for secure data storage and management. The core methodology involves three stages: face detection, feature extraction, and identity recognition. The Haar Cascade Classifier is employed for fast and accurate face detection, and the Local Binary Pattern Histogram (LBPH) algorithm is used for robust face recognition under varying environmental conditions. Attendance is automatically recorded by matching detected faces with the database, reducing human intervention and errors. Experimental evaluation shows the system achieves 95%–97% accuracy, making it highly reliable and scalable. This approach provides a cost-effective, transparent, and secure solution suitable for schools, colleges, and corporate organizations, demonstrating the potential of AI and data science to revolutionize attendance tracking while enhancing operational efficiency and security.

Image processing, Face Recognition, Computer Vision (CV), Harrcascade, LBPH.

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