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Revolutionizing Attendance Face Recognition Systems
Subject area: Science,Engineering and Technology · Area of research: Deep Learning & Face Recognition
DOI: https://doi.org/10.64388/IREV9I10-1717015
Abstract
Reliable documentation of attendance remains a persistent operational burden in educational institutions and corporate settings alike. Conventional solutions — spanning handwritten roll calls to token-based card readers — are routinely compromised by proxy attendance, labour-intensive record keeping, and limited scalability. Growing dissatisfaction with these limitations motivates the development of intelligent, low-infrastructure alternatives rooted in contemporary AI techniques. This paper proposes an automated, touchless attendance capture system founded on a sequential three-layer deep learning pipeline. The detection layer employs a Multi-Task Cascaded Convolutional Network (MTCNN) to scan live video and isolate individual faces along with associated anatomical landmarks. The representation layer then processes the geometrically normalised face crops through a FaceNet model built on an Inception-ResNet-v1 backbone, producing a compact 128-dimensional feature vector per face via triplet-loss-driven metric learning. Finally, the decision layer applies a Support Vector Machine with a Radial Basis Function kernel to associate each embedding with a registered identity that was enrolled offline. Evaluation spanned two distinct data sources: the publicly accessible Labeled Faces in the Wild (LFW) benchmark alongside an institution-specific corpus drawn from 50 volunteers photographed across varied lighting environments. The pipeline achieved 98.7% accuracy on the local dataset and 99.1% on LFW, with per-frame processing completing in 120 milliseconds. The False Acceptance Rate was confined to 0.8%. A Flask-powered administrative dashboard facilitates live monitoring and automated report export.
Keywords
Touchless Attendance, Facial Identification, MTCNN, Facenet Embeddings, SVM Classifier, Deep Metric Learning, Real-Time Recognition, Edge Deployment, Biometric Authentication.
How to cite this paper
@article{1717015,
author = {Ashish Kumar, Akash Dwivedi, Suresh Kumar Tiwari, Dr. Sanjay Pachauri},
title = {Revolutionizing Attendance Face Recognition Systems},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {3598-3603},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717015.pdf},
abstract = {Reliable documentation of attendance remains a persistent operational burden in educational institutions and corporate settings alike. Conventional solutions — spanning handwritten roll calls to token-based card readers — are routinely compromised by proxy attendance, labour-intensive record keeping, and limited scalability. Growing dissatisfaction with these limitations motivates the development of intelligent, low-infrastructure alternatives rooted in contemporary AI techniques. This paper proposes an automated, touchless attendance capture system founded on a sequential three-layer deep learning pipeline. The detection layer employs a Multi-Task Cascaded Convolutional Network (MTCNN) to scan live video and isolate individual faces along with associated anatomical landmarks. The representation layer then processes the geometrically normalised face crops through a FaceNet model built on an Inception-ResNet-v1 backbone, producing a compact 128-dimensional feature vector per face via triplet-loss-driven metric learning. Finally, the decision layer applies a Support Vector Machine with a Radial Basis Function kernel to associate each embedding with a registered identity that was enrolled offline. Evaluation spanned two distinct data sources: the publicly accessible Labeled Faces in the Wild (LFW) benchmark alongside an institution-specific corpus drawn from 50 volunteers photographed across varied lighting environments. The pipeline achieved 98.7% accuracy on the local dataset and 99.1% on LFW, with per-frame processing completing in 120 milliseconds. The False Acceptance Rate was confined to 0.8%. A Flask-powered administrative dashboard facilitates live monitoring and automated report export.},
keywords = {Touchless Attendance, Facial Identification, MTCNN, Facenet Embeddings, SVM Classifier, Deep Metric Learning, Real-Time Recognition, Edge Deployment, Biometric Authentication.},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1717015}
}