CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation

By Wen-Jing Yan, Xiaobai Li, Su-Jing Wang, Guoying Zhao, Yong-jin Liu, Yu-Hsin Chen, Xiaolan Fu. PLoS ONE, 2014

958 citations recorded by the source corpus.

A robust automatic micro-expression recognition system would have broad applications in national safety, police interrogation, and clinical diagnosis. Developing such a system requires high quality databases with sufficient training samples which are currently not available. We reviewed the previously developed micro-expression databases and built an improved one (CASME II), with higher temporal resolution (200 fps) and spatial resolution (about 280×340 pixels on facial area). We elicited participants' facial expressions in a well-controlled laboratory environment and proper illumination (such as removing light flickering). Among nearly 3000 facial movements, 247 micro-expressions were selected for the database with action units (AUs) and emotions labeled. For baseline evaluation, LBP-TOP and SVM were employed respectively for feature extraction and classifier with the leave-one-subject-out cross-validation method. The best performance is 63.41% for 5-class classification.

Subfields: Experimental and Cognitive Psychology, Information Systems, Social Psychology

View Paper: https://www.semanticscholar.org/paper/dc40dbf8b27ea76ecf3b2284de4ce5748f782598

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Citations in the corpus

SAMM: A Spontaneous Micro-Facial Movement Dataset (2018, methodology): “Some datasets [14], [16], [17] assign an emotion label to videos based on self-reports completed by participants.”
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] .”
Towards Reading Hidden Emotions: A Comparative Study of Spontaneous Micro-Expression Spotting and Recognition Methods (2015, background): “[20] collected a newer version of ME database, CASMEII, which provides more ME samples with higher spatial and temporal resolutions.”
A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition (2016, methodology): “Thanks to recent well-developed databases [7], [8], [9], the demand for computer vision techniques to improve the performance of micro-expression recognition is increasing.”
Automatic Analysis of Facial Actions: A Survey (2019, neutral): “AM-FED [117] Induced N/A Various head poses D 10 N N Bosphorous [155] On command 105 subjects 3D multi-pose S 25 N Y BP4D [218] Induced 41 subjects 3D multi-pose D 27 N Y CASME [207] Induced (micro) 35 subjects Near frontal D F Y N CASME II [206] Induced (micro) 26 subjects Near frontal D F Y N Cohn”
LBP with Six Intersection Points: Reducing Redundant Information in LBP-TOP for Micro-expression Recognition (2014, methodology): “[12] reported a baseline performance of up to 63.”
Video-Based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms (2021, methodology): “Here, we evaluate three alignment algorithms, namely ASM+LWM [32], DRMF+Optical flow alignment (OFA) [85] and joint cascade face detection and alignment (JCFDA) [113], for face alignment on the MMEW dataset.”
A Boost in Revealing Subtle Facial Expressions: A Consolidated Eulerian Framework (2019, methodology): “+ Authors contributed equally to this work § Corresponding author MAG before extracting features and improved the accuracy by around 10% on the CASMEII [34] database.”