Facial Recognition-Driven Attendance Systems: A Review of Deep Learning Approaches in Real-Time Academic Monitoring
Year: 2025
Type: article
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.
Authors: NIHAL ABOOABKER, VISHNU C
Institution: Sacred Heart College
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4415115242
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