Facial Recognition-Driven Attendance Systems: A Review of Deep Learning Approaches in Real-Time Academic Monitoring

Author(s)	NIHAL ABOOABKER, VISHNU MOHAN C
Country	India
Abstract	As universities and companies rapidly modernize, the old headache of attendance—slow roll calls and frustrating fingerprint scanners—is finally being solved by smart, touchless facial recognition systems. Our review of twenty recent studies reveals just how quickly this field is advancing, moving far beyond basic methods like Haar Cascades to employ powerful deep learning models like FaceNet and Vision Transformers. These high-tech systems aren't just accurate; they're designed for the modern environment, offering real-time performance, integration with mobile apps and the cloud, and even advanced features like emotion detection or iris scans to beat mask-wearing or spoofing attempts. With most models boasting over 90% accuracy, the technology is ready for prime time, though researchers continue to focus on fine-tuning issues like lighting conditions and ensuring absolute data privacy. Ultimately, this technology offers a clear, scalable path toward a future where attendance is instant, accurate, and completely hassle-free.
Keywords	Face Recognition, Real-Time Attendance, Deep Learning, Automation
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-10-12
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.57582

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