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Computer Vision and Pattern Recognition Based on Sensing Technology

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 237

Special Issue Editor


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Guest Editor
School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300387, China
Interests: artificial intelligence; machine learning; data processing; pattern recognition; image processing; image analysis; Industrial development planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advancement of computing hardware and sensor technologies such as imaging technologies, applications of artificial intelligence like computer vision (CV) and pattern recognition (PR) are being increasingly applied in fields such as object inspection and identification, information security monitoring, and analysis prediction. Data analysis based on sensors and images has enhanced the precision and efficiency of intelligent monitoring, detection, prediction, and identification. However, challenges remain in terms of adaptability to complex environments, data utilization, and model generalization capabilities. Researchers are exploring methods such as adaptive feature extraction and multimodal algorithm fusion to optimize model robustness and adaptability. With advancements in these technologies, the application of sensors and artificial intelligence in production, safety, and daily life will become more precise and efficient, providing strong support for intelligent detection, risk warning, and automated decision-making.

This Special Issue explores innovative solutions that use advanced artificial intelligence and computer vision technologies to address challenges in real-world applications. Topics of interest include, but are not limited to, the following:

Object detection and identification;

Image classification and analysis;

Computer vision;

Intelligent sensors and data analysis;

Image classification and detection technologies;

Fiber optic sensors;

Image sensors.

Dr. Xiao Yu
Guest Editor

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • computer vision
  • image analysis
  • intelligent sensors

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Published Papers (1 paper)

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Research

13 pages, 706 KiB  
Article
Enhancing 3D Face Recognition: Achieving Significant Gains via 2D-Aided Generative Augmentation
by Cuican Yu, Zihui Zhang, Huibin Li and Chang Liu
Sensors 2025, 25(16), 5049; https://doi.org/10.3390/s25165049 - 14 Aug 2025
Abstract
The development of deep learning-based 3D face recognition has been constrained by the limited availability of large-scale 3D facial datasets, which are costly and labor-intensive to acquire. To address this challenge, we propose a novel 2D-aided framework that reconstructs 3D face geometries from [...] Read more.
The development of deep learning-based 3D face recognition has been constrained by the limited availability of large-scale 3D facial datasets, which are costly and labor-intensive to acquire. To address this challenge, we propose a novel 2D-aided framework that reconstructs 3D face geometries from abundant 2D images, enabling scalable and cost-effective data augmentation for 3D face recognition. Our pipeline integrates 3D face reconstruction with normal component image encoding and fine-tunes a deep face recognition model to learn discriminative representations from synthetic 3D data. Experimental results on four public benchmarks, i.e., the BU-3DFE, FRGC v2, Bosphorus, and BU-4DFE databases, demonstrate competitive rank-1 accuracies of 99.2%, 98.4%, 99.3%, and 96.5%, respectively, despite the absence of real 3D training data. We further evaluate the impact of alternative reconstruction methods and empirically demonstrate that higher-fidelity 3D inputs improve recognition performance. While synthetic 3D face data may lack certain fine-grained geometric details, our results validate their effectiveness for practical recognition tasks under diverse expressions and demographic conditions. This work provides an efficient and scalable paradigm for 3D face recognition by leveraging widely available face images, offering new insights into data-efficient training strategies for biometric systems. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition Based on Sensing Technology)
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