What question did this study set out to answer?

This research aims to compare different facial recognition models for automated attendance systems in classrooms.

January 27, 20260 citations

Comparative Analysis of Facial Recognition Based Attendance System Using Machine Learning

PDPradyumna Rao D BBBalasubramanian Prabhu Kavin B

Key Points

This research aims to compare different facial recognition models for automated attendance systems in classrooms.
Analyzed four facial recognition pipelines: ArcFace, SFace, GhostFaceNet, and Dlib.
Used DeepFace and face recognition frameworks on a dataset of about 13,000 images.
Evaluated models based on identification accuracy, verification reliability, latency, and throughput.
Dlib performed best with an identification accuracy of 87.93% and an AUC of 0.9952.
Achieved a throughput of 11.53 FPS, outperforming deeper CNN-based models.
Highlighted trade-offs between accuracy, discriminability, and real-time efficiency.

Abstract

Automated attendance systems benefit from robust, scalable facial recognition models capable of handling real-world classroom variability illumination, pose, and occlusion. This study conducts a systematic comparison of four pipelines ArcFace, SFace, GhostFaceNet, and Dlib using the DeepFace and face recognition frameworks on a custom dataset of approximately 13,000 images representing 1,600 identities. Evaluation metrics include identification accuracy (Top-1), verification reliability (AUC, TAR@FAR), latency, and throughput (FPS). Dlib achieved the best overall performance (Top-1 = 87.93%, AUC = 0.9952, 11.53 FPS), outperforming deeper CNN-based embeddings in both speed and accuracy. The benchmark highlights trade-offs between accuracy, discriminability, and real-time efficiency, providing practical deployment insights for classroom automation.

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