RCF-Net: Degradation-Aware Hybrid CNN–Transformer for Child Face Identification in Surveillance

An Attention-Enhanced Lightweight CNN Framework with MTCNN Detection and TripletEmbedding Recognition for Occlusion-Robust Automated Attendance from Surveillance Video

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.

Enhancing facial expression recognition with lightweight attention-based CNN

A Hybrid CNN–Transformer Network for Robust Masked and Occluded Face Recognition in Smart Surveillance Systems

Face recognition systems applied to smart surveillance settings often experience poor performance when the faces are partially occluded by a mask or other objects. Occlusions eliminate critical facial information, which makes face identification much more difficult for traditional deep learning models. To solve this issue, a hybrid deep learning model utilizing convolutional neural networks and transformer-based attention mechanism is proposed in this study for robust masked and occluded face recognition. The framework uses the ResNet50 backbone for obtaining the discriminative local facial favorable features, and the Vision Transformer module for obtaining long-range context relationships between facial regions. In addition, an Adaptive Occlusion Attention Module is introduced to Zurcrook visible facial areas and neglect the corrupted features to occlusions. Experiments were carried out on the Real-World Masked Face Dataset (RMFD) with 1205 images of 25 identities. The proposed model attained 93.46% training accuracy and Top-1 and Top-5 recognition accuracy were 51.87% and 81.33%, respectively. Additional occlusion experiments resulted in occlusion recognition accuracy of 28.63% and cross-dataset evaluation using MaskedFace-Net resulted in an average feature similarity of 0.8288. The results show that the proposed hybrid architecture enhances the recognition robustness of masked and partially obstructed facial images facing the surveillance situation.

PERSON RE-IDENTIFICATION BASED ON DEEP LEARNING NETWORKS: A SURVEY

This survey offers an updated and focused review of deep learning-based ReID methods, encompassing research from 2020 to 2025, and investigates in-depth the engineering aspects, including system integration, real-time performance, and sensor constraints, which are often overlooked in reviews of earlier work.

Vision Transformer with Attention Rollout for Deepfake Face Image Detection and Localization

Generative AI and synthetic media generation tools have enabled widespread media manipulation tools and raised important privacy concerns with misinformation, identity fraud and the verification of authenticity of media. Most of the current convolution-based deepfake detection methods are hard to be deployed in real scenarios and hard to be interpretable, especially because they have limited ability to capture long-range spatial dependency. It introduces an explainable deepfake face image detection framework based on a vision transformer network for performing powerful binary classification of manipulated and real facial images and an explainable face image localization framework for localizing deepfake image faces. The proposed system involves a transformer-encoder backbone for extracting features through a patches-wise process, which proves suitable for modeling the subtle changes of features when the processes of manipulating the image are designed. To make the network more interpretable, and aid the understanding of the transformer attention distribution as well as localization of manipulated facial regions, a dedicated attention rollout mechanism is embedded. A dedicated rollout mechanism for attention distribution of the transformer and heatmap generating and attention spatial localization are incorporated to improve the interpretability of the network. The framework comprises an end-to-end inference pipeline, such as image preprocessing, estimation of confidence scores, fake-real classification, generation of explainable visualization and storage of prediction history using an integrated database system. An experimental evaluation shows the system can effectively detect deepfakes while also providing accurate local information as justification for classification decisions, contributing to transparency, reliability and trust towards automated synthetic media detection systems.

A Hybrid Vision Transformer and EfficientNet-B3 Framework for Facial Expression Recognition

Facial expression recognition technology is vital for security, verification, and personalization, but it faces challenges due to variations in scale, illumination, occlusion, and facial expressions. This paper presents a hybrid architecture that combines Vision Transformers (ViTs) to capture global context with EfficientNet-B3 for multi-scale feature extraction. Unlike simple concatenation, our approach projects the ViT’s [CLS] token and the EfficientNet’s global pooling features into a shared 512-dimensional space before merging, enabling better alignment of global and local features. When tested on the FERPlus dataset, it reaches an accuracy of 94.4 ± 0.3%, surpassing several recent methods, notably existing transformer- and CNN-based methods. Ablation studies show each component’s contribution, with the full model outperforming the no-fusion version by 2.6%. With around 98 million parameters and an inference time of ~23 ms per image, it balances efficiency and high performance, suitable for real-time use on suitable hardware. Evaluation via confusion matrix, t-SNE visualization, and comparisons with recent techniques such as HLA-ViT (90.13%), AU-ViT (90.15%), and CCFER (91.24%) demonstrates its robustness and discriminative feature learning. This work highlights the promise of hybrid deep learning architectures in tackling real-world facial expression recognition challenges.