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Deep Learning-Based Face Recognition and Feature Extraction: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 798

Special Issue Editors


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Guest Editor
Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Interests: image denoising; image segmentation; image super-resolution; object detection; deep learning-based filtering
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Interests: machine learning; pattern recognition; image processing; super-resolution reconstruction; face and gesture recognition; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the first edition of our Special Issue entitled “Deep Learning Based Face Recognition and Feature Extraction” (https://www.mdpi.com/journal/sensors/special_issues/BV5I1336R1), we would like to invite our colleagues again to contribute their expertise, insights, and findings in the form of original research articles and reviews for the current edition of this Special Issue.

Human faces play a central role in interpersonal communication and social relationships, which is why automatic recognition and analysis of them have attracted the attention of the computer vision community for decades. State-of-the-art facial recognition techniques enable the identification of a person for ID verification as well as recognition and understanding of their psychophysical state, essential for smooth and high-quality human–computer interaction. Efficient facial recognition, verification, and identification algorithms are essential for developing reliable access control, surveillance, and security systems. In addition, the analysis of the emotional state of users provides valuable feedback, improving the user experience in many application areas.

In recent years, a number of face recognition methods based on deep learning and various feature extraction techniques have been developed, leading to significant advances in the field. Indeed, face recognition is one of the most active areas in computer vision research, and recent advances in systems based on deep learning have significantly improved performance compared to solutions using classical machine learning and pattern recognition techniques. Against this background, this Special Issue seeks original technical and review papers that address the latest applications of deep learning, including, but not limited to, the following:

  • Face and facial landmark detection and tracking;
  • Face recognition and identification;
  • Large-scale face recognition;
  • Facial expression recognition and analysis;
  • 3D face modeling;
  • Applications of face and facial expression recognition;
  • Fusion of multiple modalities;
  • Face anti-spoofing techniques;
  • Face liveness detection from images and videos;
  • Surveillance applications;
  • Benchmarking and new protocols;
  • Psychological and behavioral analysis.

Prof. Dr. Bogdan Smolka
Dr. Karolina Nurzynska
Prof. Dr. Michal Kawulok
Guest Editors

Manuscript Submission Information

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Keywords

  • face and facial landmark detection and tracking
  • face recognition and identification
  • large-scale face recognition
  • facial expression recognition and analysis
  • 3D face modeling
  • applications of face and facial expression recognition
  • fusion of multiple modalities
  • face anti-spoofing techniques
  • face liveness detection from images and videos
  • surveillance applications
  • psychological and behavioral analysis

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Published Papers (1 paper)

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Research

26 pages, 2520 KB  
Article
Concealed Face Analysis and Facial Reconstruction via a Multi-Task Approach and Cross-Modal Distillation in Terahertz Imaging
by Noam Bergman, Ihsan Ozan Yildirim, Asaf Behzat Sahin, Hakan Altan and Yitzhak Yitzhaky
Sensors 2026, 26(4), 1341; https://doi.org/10.3390/s26041341 - 19 Feb 2026
Viewed by 534
Abstract
Terahertz (THz) sub-millimeter wave imaging offers unique capabilities for stand-off biometrics through concealment, yet it suffers from severe sparsity, low resolution, and high noise. To address these limitations, we introduce a novel unified Multi-Task Learning (MTL) network centered on a custom shared U-Net-like [...] Read more.
Terahertz (THz) sub-millimeter wave imaging offers unique capabilities for stand-off biometrics through concealment, yet it suffers from severe sparsity, low resolution, and high noise. To address these limitations, we introduce a novel unified Multi-Task Learning (MTL) network centered on a custom shared U-Net-like THz data encoder. This network is designed to simultaneously solve three distinct critical tasks on concealed THz facial data, given a limited dataset of approximately 1400 THz facial images of 20 different identities. The tasks include concealed face verification, facial posture classification, and a generative reconstruction of unconcealed faces from concealed ones. While providing highly successful MTL results as a standalone solution on the very challenging dataset, we further studied the expansion of this architecture via a cross-modal teacher-student approach. During training, a privileged visible-spectrum teacher fuses limited visible features with THz data to guide the THz-only student. This distillation process yields a student network that relies solely on THz inputs at inference. The cross-modal trained student achieves better latent space in terms of inter-class separability compared to the single-modality baseline, but with reduced intra-class compactness, while maintaining a similar success in the task performances. Both THz-only and distilled models preserve high unconcealed face generative fidelity. Full article
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