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20231226010358.0 |
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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2022.3174895
|2 doi
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| 028 |
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|a pubmed24n1136.xml
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|a (DE-627)NLM340835850
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|a (NLM)35560102
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Li, Jingting
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a CAS(ME)3
|b A Third Generation Facial Spontaneous Micro-Expression Database With Depth Information and High Ecological Validity
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| 264 |
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|c 2023
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 09.11.2023
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|a Date Revised 09.11.2023
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Micro-expression (ME) is a significant non-verbal communication clue that reveals one person's genuine emotional state. The development of micro-expression analysis (MEA) has just gained attention in the last decade. However, the small sample size problem constrains the use of deep learning on MEA. Besides, ME samples distribute in six different databases, leading to database bias. Moreover, the ME database development is complicated. In this article, we introduce a large-scale spontaneous ME database: CAS(ME) 3. The contribution of this article is summarized as follows: (1) CAS(ME) 3 offers around 80 hours of videos with over 8,000,000 frames, including manually labeled 1,109 MEs and 3,490 macro-expressions. Such a large sample size allows effective MEA method validation while avoiding database bias. (2) Inspired by psychological experiments, CAS(ME) 3 provides the depth information as an additional modality unprecedentedly, contributing to multi-modal MEA. (3) For the first time, CAS(ME) 3 elicits ME with high ecological validity using the mock crime paradigm, along with physiological and voice signals, contributing to practical MEA. (4) Besides, CAS(ME) 3 provides 1,508 unlabeled videos with more than 4,000,000 frames, i.e., a data platform for unsupervised MEA methods. (5) Finally, we demonstrate the effectiveness of depth information by the proposed depth flow algorithm and RGB-D information
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| 650 |
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4 |
|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Dong, Zizhao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lu, Shaoyuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Su-Jing
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yan, Wen-Jing
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ma, Yinhuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Ye
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Huang, Changbing
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Fu, Xiaolan
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 3 vom: 13. März, Seite 2782-2800
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
8 |
|g volume:45
|g year:2023
|g number:3
|g day:13
|g month:03
|g pages:2782-2800
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| 856 |
4 |
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|u http://dx.doi.org/10.1109/TPAMI.2022.3174895
|3 Volltext
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|d 45
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