An Empirical Study of Super-resolution on Low-resolution Micro-expression Recognition Article Swipe
Ling Zhou
,
Mingpei Wang
,
Xiaohua Huang
,
Wenming Zheng
,
Qirong Mao
,
Guoying Zhao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.10022
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.10022
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.
Related Topics
Concepts
Benchmarking
Benchmark (surveying)
Computer science
Superresolution
Resolution (logic)
Low resolution
High resolution
Artificial intelligence
Enhanced Data Rates for GSM Evolution
Pattern recognition (psychology)
Data mining
Image (mathematics)
Remote sensing
Cartography
Geography
Marketing
Business
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2310.10022
- https://arxiv.org/pdf/2310.10022
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387724579
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387724579Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2310.10022Digital Object Identifier
- Title
-
An Empirical Study of Super-resolution on Low-resolution Micro-expression RecognitionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-16Full publication date if available
- Authors
-
Ling Zhou, Mingpei Wang, Xiaohua Huang, Wenming Zheng, Qirong Mao, Guoying ZhaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2310.10022Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2310.10022Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2310.10022Direct OA link when available
- Concepts
-
Benchmarking, Benchmark (surveying), Computer science, Superresolution, Resolution (logic), Low resolution, High resolution, Artificial intelligence, Enhanced Data Rates for GSM Evolution, Pattern recognition (psychology), Data mining, Image (mathematics), Remote sensing, Cartography, Geography, Marketing, BusinessTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.becomes | 74 |
| abstract_inverted_index.complex | 11 |
| abstract_inverted_index.crowded | 22 |
| abstract_inverted_index.focused | 42 |
| abstract_inverted_index.methods | 100 |
| abstract_inverted_index.motions | 65 |
| abstract_inverted_index.primary | 166 |
| abstract_inverted_index.problem | 154 |
| abstract_inverted_index.quality | 33 |
| abstract_inverted_index.samples | 80, 144 |
| abstract_inverted_index.survey. | 129 |
| abstract_inverted_index.tackles | 106 |
| abstract_inverted_index.thereby | 151 |
| abstract_inverted_index.uncover | 164 |
| abstract_inverted_index.videos, | 38 |
| abstract_inverted_index.analysis | 96, 190 |
| abstract_inverted_index.evaluate | 140 |
| abstract_inverted_index.identify | 173 |
| abstract_inverted_index.in-depth | 127 |
| abstract_inverted_index.inherent | 59 |
| abstract_inverted_index.insights | 192 |
| abstract_inverted_index.methods, | 123 |
| abstract_inverted_index.presents | 7 |
| abstract_inverted_index.provides | 191 |
| abstract_inverted_index.samples, | 72 |
| abstract_inverted_index.scarcity | 51 |
| abstract_inverted_index.thorough | 95 |
| abstract_inverted_index.benchmark | 114 |
| abstract_inverted_index.capturing | 62 |
| abstract_inverted_index.efficient | 197 |
| abstract_inverted_index.empirical | 161 |
| abstract_inverted_index.enhancing | 31 |
| abstract_inverted_index.features. | 87 |
| abstract_inverted_index.generated | 145 |
| abstract_inverted_index.important | 9 |
| abstract_inverted_index.improving | 47 |
| abstract_inverted_index.integrate | 117 |
| abstract_inverted_index.practical | 15 |
| abstract_inverted_index.scenarios | 6 |
| abstract_inverted_index.addressing | 152 |
| abstract_inverted_index.associated | 168 |
| abstract_inverted_index.attributed | 56 |
| abstract_inverted_index.challenge, | 12 |
| abstract_inverted_index.challenges | 167, 178 |
| abstract_inverted_index.conducting | 110 |
| abstract_inverted_index.difficulty | 60 |
| abstract_inverted_index.leveraging | 180 |
| abstract_inverted_index.literature | 128 |
| abstract_inverted_index.systematic | 92 |
| abstract_inverted_index.techniques | 29, 138 |
| abstract_inverted_index.SR-assisted | 170, 198 |
| abstract_inverted_index.challenging | 77 |
| abstract_inverted_index.distinctive | 86 |
| abstract_inverted_index.experiments | 115 |
| abstract_inverted_index.investigate | 44 |
| abstract_inverted_index.performance | 142 |
| abstract_inverted_index.progressing | 194 |
| abstract_inverted_index.recognition | 1 |
| abstract_inverted_index.techniques, | 150 |
| abstract_inverted_index.Furthermore, | 88 |
| abstract_inverted_index.advancements | 26, 182 |
| abstract_inverted_index.applications | 16 |
| abstract_inverted_index.benchmarking | 93 |
| abstract_inverted_index.considerable | 25 |
| abstract_inverted_index.cutting-edge | 134 |
| abstract_inverted_index.particularly | 13 |
| abstract_inverted_index.Specifically, | 130 |
| abstract_inverted_index.environments. | 23 |
| abstract_inverted_index.investigation | 53 |
| abstract_inverted_index.low-resolution | 4 |
| abstract_inverted_index.methodologies. | 188 |
| abstract_inverted_index.Micro-expression | 0 |
| abstract_inverted_index.state-of-the-art | 135 |
| abstract_inverted_index.super-resolution | 28, 45, 119, 156 |
| abstract_inverted_index.micro-expressions, | 67 |
| abstract_inverted_index.original-resolution | 70 |
| abstract_inverted_index.super-resolution-assisted | 98 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |