Jul 2026· International Conference Computing Methodologies and Communication· pp. 1279-1284· 0 citations· 13 references
Abstract
Missing person identification using surveillance imagery remains a challenging problem due to adverse visual conditions, limited availability of reference images, and strict privacy constraints surrounding real-world data. This paper presents a hybrid deep-learning framework for missing person identification that combines YOLO-based person detection with deep face embedding models, specifically FaceNet and ArcFace. To address ethical and privacy limitations, a manually curated composite synthetic dataset is constructed by combining publicly available crowd and in-the-wild face datasets with additional curated images to realistically emulate CCTV conditions, including low illumination, occlusions, visually similar individuals, accessories, and clothing-matched decoys, while restricting each identity to only three to four reference images. The proposed system is evaluated across five YOLO variants (v8–v12) and a wide range of cosine similarity thresholds to analyze detection sensitivity, false positive behavior, and overall identification robustness. Extensive experiments conducted on 780 group images demonstrate that the YOLO–ArcFace pipeline achieves superior performance, reaching a peak identification accuracy of 97.50% with near-zero false positives, while the YOLO–FaceNet pipeline attains a maximum accuracy of 95.42% at an optimized operating threshold but exhibits higher susceptibility to false matches. Threshold–accuracy analysis and operating point comparison reveal that ArcFace embeddings provide stronger inter-class separation and greater stability under surveillance-specific distortions. The results highlight the importance of discriminative embedding models in safety-critical identification tasks and demonstrate that reliable missing person detection is achievable even under constrained and privacy-preserving settings, with future extensions proposed toward continuous video-based tracking using multi-object tracking algorithms.
Person Re-Identification_(ReID) is a crucial task in computer vision with growing importance in security and engineering applications, particularly in surveillance and smart city systems. The hand-crafted feature-based existing approaches that consider texture and color struggle with complex real challenges involving lighting; person pose; and variable backgrounds. This survey offers an updated and focused review of deep learning-based ReID methods, encompassing research from 2020 to 2025. It investigates in-depth the engineering aspects, including system integration, real-time performance, and sensor constraints, which are often overlooked in reviews of earlier work. Techniques discussed in this study involve CNNs and transformers, triplet loss and contrastive learning, GANs, and methods that enhance matching accuracy and generalization. The paper compares recent methods; presenting their strengths and weaknesses, and setting directions for future research. The survey aims to provide a practical reference for engineers and researchers interested in developing robust and scalable ReID systems in real-world environments
Z.A.M. Faisal, Nidhal K. El Abbadi· Kufa journal of Engineering· 0 citations
Child face identification from surveillance video remains difficult because facial crops are frequently low-resolution, blurred, partially occluded, and captured under unstable illumination. Age-related facial variation further increases the difficulty of maintaining discriminative identity embeddings for children. This paper presents RCF-Net, a degradation-aware hybrid CNN–Transformer architecture that combines surveillance-oriented image degradation, dual-branch local/global feature extraction, and learnable cross-attention fusion. MTCNN is used for face detection and alignment, ArcFace supervision is used for discriminative embedding learning, and DeepSORT can optionally be integrated to improve temporal identity consistency in video streams. To address deployment concerns raised by surveillance use, the revised framework also specifies age-progression handling, latency-aware scheduling for live video, multi-camera scaling, and adversarial/spoof-risk safeguards. Experiments are conducted using public face datasets, namely VGGFace2, CASIA-WebFace, CelebA, and IMDB-WIKI, with child-oriented filtering and synthetic surveillance degradations. Compared with representative CNN, transformer, and hybrid baselines, RCF-Net achieves the best overall accuracy of 91.4% and yields the strongest robustness under low-resolution, blur, and occlusion stress tests. The results indicate that explicit degradation modeling and local-global feature fusion are complementary for surveillance-oriented child face identification.
R. Arora, Akash Pandey, Navjeet Kaur· 2026 4th International Confe...· 0 citations
Face recognition in unconstrained environments remains a challenging problem in computer vision due to variations in pose, illumination, expression, and occlusion. This paper proposes a novel attention-enhanced ArcFace-based deep learning framework that integrates a Residual CNN backbone with Convolutional Block Attention Module (CBAM) and ArcFace loss for robust face recognition. Unlike existing approaches that rely on large-scale external pretraining datasets, the proposed framework is trained exclusively on the Labelled Faces in the Wild (LFW) dataset, demonstrating data-efficient learning. The system is evaluated on both 1:1 verification and 1:N identification protocols. Experimental results demonstrate superior performance with verification accuracy of 95.70%, identification accuracy of 89.75%, ROC-AUC of 99.16%, and True Positive Rate (TPR) of approximately 92% at a 1% False Positive Rate (FPR). The novelty lies in the synergistic integration of attention mechanisms with angular margin-based metric learning, achieving competitive performance without external pretraining. Comparative analysis with state-of-the-art methods including DeepFace, FaceNet, VGGFace, SphereFace, and baseline ArcFace validates the effectiveness of the proposed attention-guided approach for unconstrained face recognition tasks.
Samadhan S. Ghodke, Prapti D. Deshmukh· International journal of com...· 0 citations
In forensic environments, automated identification of perpetrators is difficult due to pose changes, changes in light, occlusion, and lack of labeled data. This paper presents ForensicNet, a lightweight deep learning framework for forensic face recognition that enhances attention. The suggested model combines the MobileNetV2 backbone with Convolutional Block Attention Modules (CBAM) to improve the learning of discriminative features while maintaining computational speed. A two-phase transfer learning strategy with adaptive layer unfreezing is used to improve domain adaptation and reduce overfitting. This study used publicly available datasets such as LFW and SCFace, with 15,000 facial images spanning 68 identity classes. The proposed model outperforms baseline architectures such as AlexNet, ResNet-50, and MobileNetV2, with an accuracy of 92.4%, a precision of 90.8%, and a recall of 89.5%. Additionally, the framework requires only 2.1 GFLOPs per inference, and hence can be used in real-time forensic surveillance applications.
S. N. J., L. B. T.· Engineering, Technology &...· 0 citations
The increasing use of vlog videos on social media creates privacy risks because third-party faces are often unintentionally recorded and distributed without consent. Existing face blurring approaches generally apply uniform anonymization to all detected faces and do not provide an identity-selective mechanism that keeps the content creator visible while blurring other individuals. This study develops PRIVA, a desktop-based selective face blurring application that runs locally without an external AI server. The proposed pipeline integrates YOLOv8n-Face-960 for face detection, MobileFaceNet for face recognition using 512-dimensional embeddings, and Deep SORT for maintaining identity consistency across video frames. Face enrollment is performed through guided multi-pose webcam capture, while video evaluation is conducted on extracted YOLO analysis frames from five real vlog-like test videos. YOLOv8n-Face-960 achieved an overall detection precision of 95.02%, recall of 89.32%, and F1-score of 92.09%. The baseline comparison showed that YOLOv8n-Face-960 achieved a higher mean detection F1-score than MTCNN, while MobileFaceNet provided a smaller and faster recognition model than FaceNet for CPU-based local inference. For correctly detected face instances, PRIVA achieved a system precision of 99.45%, recall of 98.70%, F1-score of 99.08%, and accuracy of 98.50% in determining whether faces should be blurred or kept visible. Processing performance testing showed an average analysis speed of 4.83 FPS, average export speed of 70.05 FPS, and average processing ratio of approximately 2.40 times the original video duration. These results indicate that PRIVA can support practical local identity-selective face blurring for video privacy protection, although detection robustness remains important under low-light, crowded, distant, or partially occluded face conditions.
Muhammad Satrio, Mohammad Nasucha· SinkrOn· 0 citations
A survey of demographic attribute estimation from periocular images, covering publicly available datasets, methodological trends from handcrafted descriptors to deep learning architectures, and the state of the art in gender, age, and ethnicity prediction is provided.
F. Alonso-Fernandez, Kevin Hernandez-Diaz, J. Bigun· 0 citations