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@article{200346,
        author = {Pydi Praneethika and Dr. ummadi Sathish Kumar and Singamplli Bala Venkataraju and Pasumarthi Jeevan Kumar and Edulamudi Swathi},
        title = {Ai Powered Face Recognition Attendance System with Microservices Architecture And Real-Time Whatsapp Integration},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {1105-1110},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200346},
        abstract = {Traditional attendance management systems based on manual roll calls, RFID cards, and biometric methods suffer from inefficiency, proxy attendance, and scalability limitations. This paper presents an AI-Powered Face Recognition Attendance System with Microservices Architecture and Real-Time WhatsApp Integration that automates attendance management using deep learning, distributed microservices, and real-time communication. The system employs InsightFace for face detection and recognition using 512-dimensional facial embeddings and cosine similarity matching. A multi-sample registration process improves recognition reliability, while a confidence-based manual verification mechanism reduces false positives. The proposed architecture adopts a polyglot persistence strategy using MySQL, MongoDB, Redis, and Cloudinary for optimized data handling. Real-time absence alerts and attendance reports are delivered through WhatsApp integration using Twilio. Experimental results demonstrate 95% reduction in attendance marking time, 90.1% recognition accuracy, and stable support for 500 concurrent users. The proposed system provides a scalable, secure, and intelligent solution for modern automated attendance management.},
        keywords = {Face Recognition, Attendance System, Microservices Architecture, Deep Learning, WhatsApp Integration.},
        month = {May},
        }