Authors : Keerthana B.; Lohith C.

Volume/Issue : Volume 11 - 2026, Issue 8 - August

Google Scholar : https://tinyurl.com/5dkfhxhw

Scribd : https://tinyurl.com/4mbpzctk

DOI : https://doi.org/10.38124/ijisrt/26aug455

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Manual and card/barcode-based attendance recording remains slow, error-prone, and vulnerable to proxy marking, motivating fully automated, camera-based alternatives for schools and organizations. This paper proposes an Attention-Enhanced Lightweight CNN framework that couples MTCNN multi-scale face detection with a CBAM (Convolutional Block Attention Module) augmented MobileFace-style backbone trained under triplet loss to produce compact, discriminative 128-dimensional face embeddings from surveillance video. Enrolled identities are matched via cosine similarity against a reference embedding gallery, and a temporal multi-frame voting stage consolidates predictions across consecutive frames to suppress transient misdetections caused by pose change, partial occlusion, or motion blur.

Keywords : Automated Attendance, Convolutional Neural Networks, Face Recognition, Surveillance Video, MTCNN, Attention Mechanism, Triplet Loss, Deep Learning, Occlusion Robustness.

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