Recognising spontaneous facial micro-expressions

By Tomas Pfister, Xiaobai Li, Guoying Zhao, Matti Pietikäinen. Vision, 2011

448 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/64fe14cb57889f95b770b088dc846671ea644769

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In Cognitive Neuroscience

In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

Citations in the corpus

A Spontaneous Micro-expression Database: Inducement, collection and baseline (2013, methodology): “The past work on micro-expression recognition [23] applied this method on only six participants’ data using the same methods for feature extraction as in this paper.”
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications (2016, background): “Detection and classification of posed [246], [247], [249] and more recently spontaneous microexpressions [248] have been proposed.”
A Boost in Revealing Subtle Facial Expressions: A Consolidated Eulerian Framework (2019, methodology): “[24] proposed to use Time Interpolation Model (TIM) [38], [37] for MER problem, which allows to obtain sufficient frames for feature extraction even for very short expressions.”
SAMM: A Spontaneous Micro-Facial Movement Dataset (2018, methodology): “Recent research has resulted in different methods for extracting micro-expressions from the face [18], [19], [20] in an attempt to support human decisions.”
Automatic Analysis of Facial Actions: A Survey (2019, background): “first research question relates to features that capture such changes [139].”
CASME database: A dataset of spontaneous micro-expressions collected from neutralized faces (2013, background): “An interrogation room setting with a punishment threat and highly emotional clips were chosen to create a highstake situation where participants undergoing high emotional arousal are motivated to suppress their facial expressions [10].”
A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition (2016, methodology): “We evaluate our proposed method on three spontaneous micro-expression databases (SMIC, CASME, and CASME II) and compare MDMO with two state-of-the-art baseline features (HOOF [22] and LBP-TOP [13], [23]).”
Video-Based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms (2021, methodology): “Two interpolation algorithms, namely Newton interpolation [70] and TIM interpolation [115], are used to interpolate micro-expression sequences into different numbers of frames, including 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110 frames.”
CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation (2014, methodology): “Pfister et al. [8] proposed a framework using temporal interpolation model and multiple kernel learning to recognize micro-expressions.”