An Attention-Enhanced Lightweight CNN Framework with MTCNN Detection and TripletEmbedding Recognition for Occlusion-Robust Automated Attendance from Surveillance Video
Aug 2026· International Journal of Innovative Science and Research Technology· pp. 750· 0 citations· 18 references
TL;DR
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 is proposed.
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.
The proposed Optimized Multi-level Mixed Attention-enabled Hybrid Learning-based Bidirectional Gradient (OM2AHL-BiG) model results in enhanced detection, adaptability to various forgery types, and improved interpretability, making it an effective solution for detecting deepfake and intra-frame video forgeries.
Automated exam proctoring under overhead surveillance is challenging due to desk occlusion, privacy concerns, rare cheating events, and limited manual annotations. To address these issues, we propose a noise-aware proctoring framework based on Temporal Kinematic Network (TKNet), a topology-independent backbone that foc...
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A Deepfake detection framework based on MobileNetV2, which uses effective convolutional feature extraction to accurately classify real and fake facial photos to guarantee consistent input quality and improve the discriminative features for detection is suggested.
A. Mohitha, C. B. Jones· International Journal of Eng...· 0 citations
A lightweight FER model that combines a truncated MobileNetV2 backbone with a patch-based local feature extraction module and a channel-attention refinement module and a channel-attention refinement module, followed by a compact classifier is proposed, outperforming many existing lightweight models.
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