• Manda Manoj Kumar
• Gade Rohith Reddy
• Mahankali Likhith
• Mettu Hemanth
• Dr. G Prabhakar Raju

In the pursuit of streamlining attendance management processes, this project introduces an Automated Face Recognition Attendance System leveraging cutting-edge deep learning techniques. The system, developed using Python, OpenCV, and incorporating the Face_recognition and dlib libraries, harnesses the capabilities of Convolutional Neural Network (CNN) and K-Nearest Neighbors (KNN) algorithms. Operationalizing the system begins with capturing facial images via a camera, followed by preprocessing steps to enhance image quality and feature extraction. The CNN model then performs intricate analysis and classification of facial features, while the KNN algorithm refines recognition accuracy. This amalgamation of technologies enables real-time attendance tracking, eradicating manual data entry and mitigating errors. The project endeavors to revolutionize attendance management paradigms, offering an intuitive, efficient solution that optimizes resource allocation and enhances productivity. Experimental evaluations showcase the system's efficacy in terms of accuracy, speed, and scalability, paving the way for widespread adoption across diverse educational and organizational domains.

Convolutional Neural Network, deep learning, K nearest neighbors, face_recognition, dlib

"Automated Face Recognition Attendance System Using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.f298-f306, February-2024, Available :http://www.jetir.org/papers/JETIR2402537.pdf

"Automated Face Recognition Attendance System Using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppf298-f306, February-2024, Available at : http://www.jetir.org/papers/JETIR2402537.pdf