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An Empirical Study of Super-resolution on Low-resolution Micro-expression Recognition

arXiv 2310.10022 ↗First posted 16 Oct 2023cs.CVcited by 0

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Abstract (arXiv)

Micro-expression recognition (MER) in low-resolution (LR) scenarios presents an important and complex challenge, particularly for practical applications such as group MER in crowded environments. Despite considerable advancements in super-resolution techniques for enhancing the quality of LR images and videos, few study has focused on investigate super-resolution for improving LR MER. The scarcity of investigation can be attributed to the inherent difficulty in capturing the subtle motions of micro-expressions, even in original-resolution MER samples, which becomes even more challenging in LR samples due to the loss of distinctive features. Furthermore, a lack of systematic benchmarking and thorough analysis of super-resolution-assisted MER methods has been noted. This paper tackles these issues by conducting a series of benchmark experiments that integrate both super-resolution (SR) and MER methods, guided by an in-depth literature survey. Specifically, we employ seven cutting-edge state-of-the-art (SOTA) MER techniques and evaluate their performance on samples generated from 13 SOTA SR techniques, thereby addressing the problem of super-resolution in MER. Through our empirical study, we uncover the primary challenges associated with SR-assisted MER and identify avenues to tackle these challenges by leveraging recent advancements in both SR and MER methodologies. Our analysis provides insights for progressing toward more efficient SR-assisted MER.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 31 of its 71 references that are in the map. First posted on arXiv 16 Oct 2023.

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31 of 71 references.

References · 31

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  3. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
  4. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,887
  5. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
  6. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,254
  7. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,224
  8. 2021Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Datacited by 970
  9. 2018Residual Dense Network for Image Super-Resolutioncited by 799
  10. 2021Designing a Practical Degradation Model for Deep Blind Image Super-Resolutioncited by 529
  11. 2019EDVR: Video Restoration with Enhanced Deformable Convolutional Networkscited by 494
  12. 2019Deep Learning for Image Super-resolution: A Surveycited by 369
19 more
  1. 2021BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignmentcited by 342
  2. 2020BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyondcited by 304
  3. 2019Blind Super-Resolution With Iterative Kernel Correctioncited by 203
  4. 2017Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognitioncited by 178
  5. 2020GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolutioncited by 158
  6. 2021Investigating Tradeoffs in Real-World Video Super-Resolutioncited by 134
  7. 2020Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark Estimationcited by 53
  8. 2018OFF-ApexNet on Micro-expression Recognition Systemcited by 43
  9. 2021Deep Learning-based Face Super-Resolution: A Surveycited by 32
  10. 2020Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognitioncited by 31
  11. 2021Best-Buddy GANs for Highly Detailed Image Super-Resolutioncited by 30
  12. 2021Omniscient Video Super-Resolutioncited by 30
  13. 2020ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Networkcited by 27
  14. 2019Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressionscited by 25
  15. 2021Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognitioncited by 25
  16. 2023DFME: A New Benchmark for Dynamic Facial Micro-expression Recognitioncited by 13
  17. 2023High-Similarity-Pass Attention for Single Image Super-Resolutioncited by 11
  18. 2022Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolutioncited by 9
  19. 2016Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis & Applicationcited by 7

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