Advanced Face Recognition Technology in Computer Vision

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".

Deadline for manuscript submissions: 15 November 2026 | Viewed by 551

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Department of Cybersecurity, Sydney International School of Technology and Commerce, Sydney, NSW 2000, Australia
Interests: cybersecurity; artificial intelligence; face recognition; information security; steganography; steganalysis; cryptography; digital forensics
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Special Issue Information

Dear Colleagues,

Face recognition has become one of the most active research areas in computer vision, driven by rapid advancements in deep learning, edge computing, and multimodal data fusion. This Special Issue aims to bring together cutting-edge research and novel developments that push the boundaries of face recognition technology in terms of accuracy, robustness, privacy, and real-world applicability. We invite high-quality contributions addressing theoretical foundations, algorithmic innovations, and emerging applications across diverse domains such as security, biometrics, healthcare, human–computer interaction, and smart environments.

The scope of this Special Issue includes, but is not limited to, the following:

  • Deep learning architectures and transformer-based models for face recognition;
  • Lightweight and efficient face recognition for embedded and edge devices;
  • Three-dimensional face modelling, reconstruction, and recognition under unconstrained conditions;
  • Adversarial robustness, fairness, and privacy-preserving methods;
  • Multimodal and cross-domain face recognition (e.g., thermal, sketch, or audio-assisted);
  • Face recognition in video, low-light, and occluded scenarios;
  • Ethical, legal, and societal implications of face recognition technologies.

This Special Issue aims to provide a comprehensive forum for researchers, practitioners, and industry experts to share innovative ideas and foster collaborations that advance the next generation of face recognition systems.

Dr. Saman Shojae Chaeikar
Guest Editor

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Keywords

  • face recognition
  • computer vision
  • deep learning
  • transformer models
  • 3D face analysis
  • multimodal biometrics
  • adversarial robustness
  • privacy preservation
  • facial reconstruction
  • cross-domain recognition
  • human–computer interaction
  • ethical AI
  • real-world face recognition systems

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Published Papers (1 paper)

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Research

30 pages, 5511 KB  
Article
Skin Classification for Face Recognition Based on Deep Learning with U-Net and ResNet
by Sasan Karamizadeh and Saman Shojae Chaeikar
Electronics 2026, 15(9), 1950; https://doi.org/10.3390/electronics15091950 - 4 May 2026
Viewed by 251
Abstract
Face recognition under uncontrolled lighting remains challenging due to variations in brightness, background noise, and low-quality features. This paper presents a unified deep learning model that integrates illumination normalization, skin-aware spatial modulation, and quality-based margin learning within a single inference process. Unlike earlier [...] Read more.
Face recognition under uncontrolled lighting remains challenging due to variations in brightness, background noise, and low-quality features. This paper presents a unified deep learning model that integrates illumination normalization, skin-aware spatial modulation, and quality-based margin learning within a single inference process. Unlike earlier methods that treat relighting or segmentation as preprocessing, this approach directly integrates mask-guided feature modulation into embedding learning. The system comprises RetinaFace detection, photometric augmentation during training, lightweight neural relighting at inference, U-Net-based skin segmentation, and identity embeddings trained with ArcFace, AdaFace, or MagFace losses, with angular margins adapted to feature quality. Experiments on Labeled Faces in the Wild (LFW), Celebrities in Frontal-Profile (CFP-FP), Age Database 30 (AgeDB-30), and a custom illumination dataset demonstrate steady enhancements in difficult lighting conditions. The model reaches a competitive 99.8% accuracy on LFW and shows notable improvements on pose-hard CFP-FP and the custom dataset, such as a +2.6% increase in TPR at 1 × 104 FPR. The key innovations include: (i) mask-guided embedding modulation that embeds segmentation into feature learning, (ii) a dual strategy combining training-time photometric data augmentation with inference-time neural relighting, and (iii) joint spatial–quality margin learning via AdaFace/MagFace. Finally, results confirm consistent gains under challenging illumination and pose variations. Full article
(This article belongs to the Special Issue Advanced Face Recognition Technology in Computer Vision)
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