An Empirical Study of Super-resolution on Low-resolution
  Micro-expression Recognition
Ling Zhou Mingpei Wang Xiaohua Huang Wenming Zheng Qirong Mao Guoying Zhao
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.