aspg-18-4153-1792025555 20261015005235 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 17 1 Mohammed Ahmed Talab Department of medical Physics, College of Applied Science, University of Fallujah, Anbar, 00964, Iraq Mustafa A. Feath Director of the Department of Studies and Planning, College of Medicine, University of Anbar, Anbar, 00964, Iraq Ahmed Hadi Ali AL-Jumaili College of Information Technology, University of Fallujah, Anbar, 00964, Iraq Mohammed A. Al-shibl Director of Computer Center, University of Fallujah Anbar, 00964, Iraq Ravie Chandren Muniyandi College of Computing & Informatics (CCI), Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia Normal false false false EN-US X-NONE AR-SA DefSemiHidden="false" DefQFormat="false" DefPriority="99" LatentStyleCount="371"> UnhideWhenUsed="true" QFormat="true" Name="heading 2"/> UnhideWhenUsed="true" QFormat="true" Name="heading 3"/> UnhideWhenUsed="true" QFormat="true" Name="heading 4"/> UnhideWhenUsed="true" QFormat="true" Name="heading 5"/> UnhideWhenUsed="true" QFormat="true" Name="heading 6"/> UnhideWhenUsed="true" QFormat="true" Name="heading 7"/> UnhideWhenUsed="true" QFormat="true" Name="heading 8"/> UnhideWhenUsed="true" QFormat="true" Name="heading 9"/> Name="index 1"/> Name="index 2"/> Name="index 3"/> Name="index 4"/> Name="index 5"/> Name="index 6"/> Name="index 7"/> Name="index 8"/> Name="index 9"/> 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{mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Aptos",sans-serif; mso-ascii-font-family:Aptos; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Aptos; mso-hansi-theme-font:minor-latin; mso-font-kerning:1.0pt; mso-ligatures:standardcontextual;} This paper introduces an advanced method for face recognition in video surveillance systems, leveraging only a single reference image per individual. The challenge of recognizing faces in video is addressed, considering issues like pose variations, occlusions, and lighting changes. The proposed approach utilizes 3D Morphable Models (3DMM) to generate a 3D face mesh from the reference image, facilitating robust face alignment and recognition across video frames. A Convolutional Neural Network based pipeline is employed for face detection, pose estimation, and extraction of invariant features, while an optimization framework refines landmark positions and depth maps for accurate 3D reconstruction. The system performs exceptionally well on the CASIA-WebFace Dataset, with 97.00% pAUC (20%) in surveillance mode and 98.69% in identification mode for frontal views. With an efficiency of 16.72 FPS on modest hardware, the system proves its practicality for real-world deployment. The method incorporates synthetic data augmentation and Random Subspace Methods to enhance adaptability to domain-specific conditions. Compared to existing methods like Eoe-SVM and CCM-CNN, the proposed system demonstrates a superior balance between accuracy and computational efficiency, particularly in Single Sample Per Person (SSPP) scenarios. By focusing on single-reference image recognition, the system offers a promising solution for large-scale surveillance applications, where video footage typically contains multiple poses, expressions, and lighting variations. The results highlight the system's effectiveness and efficiency, making it an excellent alternative for real-time face recognition in complex and dynamic surveillance environments. 2025 325 341 4153 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.170123 https://americaspg.com/journal/18/article/4153 https://americaspg.com/storage/51760644477.pdf