• Prof. S. S. Wagh
• Kaveri Pawar
• Sakshi Dhikale
• Priya Ahire
• Snehal Kale

In the quest for efficient and precise attendance management, traditional methods often fall short due to their manual nature and susceptibility to errors. To address these challenges, we present a Multi-Face Recognition Attendance System that harnesses the power of Machine Learning to automate and enhance the attendance tracking process. Central to our system are two advanced techniques: the Haar Cascade classifier for face detection and the Local Binary Patterns Histograms (LBPH) classifier for face recognition. The Haar Cascade classifier is adept at identifying and locating faces in images or live video feeds, even under varying conditions and angles. Once faces are detected, the LBPH classifier steps in to match these faces against a pre-existing database, ensuring accurate identification and verification of individuals. This approach is particularly effective in environments where multiple individuals need to be recognized simultaneously, such as classrooms or corporate settings. The system is implemented as a web application using Streamlit, known for its simplicity and efficiency in building interactive applications. Streamlit provides a user-friendly interface where users can upload images or connect live cameras to capture real-time data for attendance tracking. The application not only simplifies the process of recording attendance but also offers easy management and review of attendance records. Our system is designed to be scalable, accommodating a growing number of users and faces, and can be integrated with existing data management infrastructure. Future improvements will aim at refining the accuracy of face detection and recognition, incorporating real-time monitoring features, and expanding compatibility with other biometric systems. By leveraging these state-of-the-art techniques within a practical and accessible platform, our Multi-Face Recognition Attendance System offers a modern solution to the challenges of attendance management, promising both increased efficiency and reliability.

Multi-Face Recognition, Machine Learning, Haar Cascade, LBPH Classifier, Streamlit Web App, Attendance Management.

"MULTI-FACE RECOGNITION ATTENDANCE SYSTEM USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.c679-c682, November-2024, Available :http://www.jetir.org/papers/JETIR2411290.pdf

"MULTI-FACE RECOGNITION ATTENDANCE SYSTEM USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppc679-c682, November-2024, Available at : http://www.jetir.org/papers/JETIR2411290.pdf