A Spontaneous Micro-expression Database: Inducement, collection and baseline

By Xiaobai Li, Tomas Pfister, Xiaohua Huang, Guoying Zhao, Matti Pietikäinen. IEEE International Conference on Automatic Face & Gesture Recognition, 2013

650 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Information Systems

View Paper: https://www.semanticscholar.org/paper/3e16a068b5ad2317b0dc373501ffbc1834f6c3af

How researchers see this paper

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In Computer Vision and Pattern Recognition

1. Yong-jin Liu
2. Xiaopeng Hong
3. Wei Peng
4. Yingyue Xu
5. Su-Jing Wang
6. John See
7. Raphael Chung-Wei Phan
8. Xiaolan Fu

In Experimental and Cognitive Psychology

1. Yong-jin Liu
2. John See
3. Raphael Chung-Wei Phan
4. Guoying Zhao
5. Xiaopeng Hong
6. Wei Peng
7. Yingyue Xu
8. Su-Jing Wang

In Information Systems

1. Yong-jin Liu
2. Guoying Zhao
3. Sze‐Teng Liong
4. John See
5. Raphael Chung-Wei Phan
6. Wei Peng

Citations in the corpus

• A Boost in Revealing Subtle Facial Expressions: A Consolidated Eulerian Framework (2019, methodology): “Methods [15], [34], [13], [18], [14], [12], [10], [11] also follow a similar mechanism and get promising results.”
• SAMM: A Spontaneous Micro-Facial Movement Dataset (2018, methodology): “TheSMICdataset [14] consists of 164 spontaneous micro-expressions filmed at 100 fps and was one of the first to include spontaneous micro-expressions obtained through emotional inducement experiments.”
• Less is More: Micro-expression Recognition from Video using Apex Frame (2016, methodology): “S eq u en ce -b a se d 1 LBP-TOP [9, 14] .”
• OFF-ApexNet on Micro-expression Recognition System (2018, methodology): “Specifically, both the SMIC [16] and CASME II [15] utilized Active Shape Model (ASM) [52] to allocate the 68 facial landmark points then Local Weighted Mean (LWM) [53] is employed to transform the faces based on the model face.”
• Video-Based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms (2021, methodology): “Since 2011, nine representative micro-expression datasets have been developed: namely, USF-HD [36], Polikovsky’s dataset [23], York DDT [37], MEVIEW [28], SMIC [30], CASME [31], CASME II [32], SAMM [29] and CAS(ME) 2 [33].”
• A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition (2016, methodology): “CASME II was developed in [7] and has improved video quality and sample size compared with CASME: More details of facial muscle movement: compared to 100 fps in SMIC and 60 fps in CASME, the recording rate in CASME 2 is 200 fps; A larger face size in video clips: compared to 190 Â 230 pixels of faci”