CASME database: A dataset of spontaneous micro-expressions collected from neutralized faces

By Wen-Jing Yan, Qi Wu, Yong-jin Liu, Sujing Wang, Xiaolan Fu. IEEE International Conference on Automatic Face & Gesture Recognition, 2013

398 citations recorded by the source corpus.

Micro-expressions are facial expressions which are fleeting and reveal genuine emotions that people try to conceal. These are important clues for detecting lies and dangerous behaviors and therefore have potential applications in various fields such as the clinical field and national security. However, recognition through the naked eye is very difficult. Therefore, researchers in the field of computer vision have tried to develop micro-expression detection and recognition algorithms but lack spontaneous micro-expression databases. In this study, we attempted to create a database of spontaneous micro-expressions which were elicited from neutralized faces. Based on previous psychological studies, we designed an effective procedure in lab situations to elicit spontaneous micro-expressions and analyzed the video data with care to offer valid and reliable codings. From 1500 elicited facial movements filmed under 60fps, 195 micro-expressions were selected. These samples were coded so that the first, peak and last frames were tagged. Action units (AUs) were marked to give an objective and accurate description of the facial movements. Emotions were labeled based on psychological studies and participants' self-report to enhance the validity.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/6c8ef3d5a4d50a348400203c9b19ebcec900db1a

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Citations in the corpus

SAMM: A Spontaneous Micro-Facial Movement Dataset (2018, methodology): “Toaddress the low number of micro-expressions in previous datasets, the CASME dataset [16] captured 195 spon-taneous micro-expressions at 60 fps, but the facial area was lower than SMIC at 150 (cid”
A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition (2016, methodology): “To the best knowledge of the authors, there are only five publicly-available micro-expression databases (USF- HD [14], Polikovsky's database [15], SMIC [8], CASME [9], [16] and CASME II [7]), and only three of them (SMIC, CASME and CASME II) are spontaneous.”
CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation (2014, methodology): “Participants are required to control and suppress their facial expressions, which often makes spontaneous micro-expressions appear partial and sometimes with only a single AU (also see Porter, 2008; Yan et al., 2013).”
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].”
Less is More: Micro-expression Recognition from Video using Apex Frame (2016, background): “Nonetheless, since 2013, the emergence of three prominent spontaneous facial microexpression databases – the SMIC from University of Oulu [14] and the CASME/ CASME II/ CAS(ME)(2) [9, 15, 16] from the Chinese Academy of Sciences, have breathed fresh interest into this domain.”
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications (2016, background): “CASME [201] is an example of a dataset containing microexpressions.”