Generative Adversarial Lightweight Classroom Face Recognition and Hierarchical Reshaping Optimization Model

Digital Technologies Research and Applications

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Generative Adversarial Lightweight Classroom Face Recognition and Hierarchical Reshaping Optimization Model

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https://doi.org/10.54963/dtra.v5i1.1793
Yang, X., & Raga Jr., R. C. (2026). Generative Adversarial Lightweight Classroom Face Recognition and Hierarchical Reshaping Optimization Model. Digital Technologies Research and Applications, 5(1), 118–133. https://doi.org/10.54963/dtra.v5i1.1793
Volume 5 Issue 1: March 2026 (in progress)
Copyright (c) 2026 Xuliang Yang, Rodolfo C. Raga Jr.
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Authors

  • Xuliang Yang

    University and Urban Integration Development Research Center, Dongguan City University, Dongguan 523000, China
    College of Computing & Information Technologies, National University, Manila 0900, Philippines
  • Rodolfo C. Raga Jr.

    College of Computer Studies and Engineering, Jose Rizal University, Mandaluyong 0900, Philippines

Received: 30 October 2025; Revised: 10 December 2025; Accepted: 27 January 2026; Published: 10 February 2026

To address the significant decline in face recognition performance caused by low resolution, high noise, and complex degradation factors in security surveillance scenarios, this paper proposes a joint optimization framework that integrates a Transformer and a Generative Adversarial Network (GAN). The innovation of this framework lies in: (1) designing the Face Reconstruction Transformer (FRFormer), which integrates a hierarchical window attention mechanism and a multi-level feature pyramid structure, enhancing the ability to retain identity features through local-global collaborative modeling; (2) constructing the GFP-GAN reconstruction model, which combines pre-trained face priors and degradation removal modules, and utilizes adversarial training to improve image authenticity and detail restoration. Experiments show that when the input is 32 × 32 pixels, the PSNR of GFP-GAN is increased by more than 8 dB, and the SSIM reaches 0.953; FRFormer achieves recognition accuracies of 99.58% and 96.31% on the LFW and AgeDB-30 benchmarks, respectively, which are 0.08 and 0.13 percentage points higher than those of Swin Transformer. Ablation experiments verify the effectiveness of the window attention mechanism and hierarchical reconstruction strategy, especially in noise suppression and cross-pose recognition tasks. This framework has broad application potential in degraded visual conditions, such as biometric recognition and medical image analysis, and provides an end-to-end solution for low-quality face recognition.

Keywords:

Generative Adversarial Networks (GANs) Transformer Architecture Face Reconstruction Window Attention Mechanism

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