Facial micro-expressions recognition using high speed camera and 3D-gradient descriptor

By Senya Polikovsky, Yoshinari Kameda, Yuichi Ohta. International Conferences on Imaging for Crime Detection and Prevention, 2009

319 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/36da2650803d67719b14deed2bfe052357ef7d7b

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In Artificial Intelligence

1. Su-Jing Wang
2. John See
3. Xiaolan Fu
4. ByoungChul Ko
5. Wei Peng
6. Matti Pietikäinen
7. Xiaobai Li
8. Kidiyo Kpalma

In Computer Vision and Pattern Recognition

1. Guoying Zhao
2. Yong-jin Liu
3. Xiaopeng Hong
4. Wei Peng
5. Xiaobai Li
6. Yingyue Xu
7. ByoungChul Ko
8. Su-Jing Wang

In Experimental and Cognitive Psychology

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

Citations in the corpus

• A Spontaneous Micro-expression Database: Inducement, collection and baseline (2013, methodology): “[21] recorded micro-expressions from ten participants using a 200fps high speed camera,”
• Towards Reading Hidden Emotions: A Comparative Study of Spontaneous Micro-Expression Spotting and Recognition Methods (2015, methodology): “[23], [31] used a 3D gradient descriptor for the recognition of AU-labeled MEs.”
• Video-Based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms (2021, methodology): “[23], [78] proposed to use the gradient histogram descriptor and K-Means algorithm to locate the onset, apex and offset frames of posed micro-expressions.”
• CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation (2014, methodology): “Polikovsky et al. [9] used a 3D-gradient descriptor for micro-expression recognition.”
• CASME database: A dataset of spontaneous micro-expressions collected from neutralized faces (2013, unknown): “Camera settings are: 480× 640 resolution, 200fps [12].”
• SAMM: A Spontaneous Micro-Facial Movement Dataset (2018, methodology): “Polikovsky et al. [13], [25] then extended this to a temporal descriptor that attempted to model micro-expressions.”
• A Boost in Revealing Subtle Facial Expressions: A Consolidated Eulerian Framework (2019, background): “Generally, ME only lasts approximately from 1/25 to 1/3 seconds [25].”
• Less is More: Micro-expression Recognition from Video using Apex Frame (2016, background): “USF-HD [11] and Polikovsky’s [12] databases, which went against the involuntary and spontaneous nature of micro-expressions [13].”
• A Brief Review of Facial Emotion Recognition Based on Visual Information (2018, methodology): “However, because CNN-based methods cannot reflect temporal variations in the facial components, a recent hybrid approach combining a CNN for the spatial features of individual frames, and long short-term memory (LSTM) for the temporal features of consecutive frames, was developed.”
• A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition (2016, background): “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.”