Paper Info
Title
Quantifying Micro-Expressions With Constraint Local Model And Local Binary Pattern
Abstract
Micro-expression may reveal genuine emotions that people try to conceal. However, it's difficult to measure it. We selected two feature extraction methods to analyze micro-expressions by assessing the dynamic information. The Constraint Local Model (CLM) algorithm is employed to detect faces and track feature points. Based on these points, the ROIs (Regions of Interest) on the face are drawn for further analysis. In addition, Local Binary Pattern (LBP) algorithm is employed to extract texture information from the ROIs and measure the differences between frames. The results from the proposed methods are compared with manual coding. These two proposed methods show good performance, with sensitivity and reliability. This is a pilot study on quantifying micro-expression movement for psychological research purpose. These methods would assist behavior researchers in measuring facial movements on various facets and at a deeper level.
Year
DOI
Venue
2014
10.1007/978-3-319-16178-5_20
COMPUTER VISION - ECCV 2014 WORKSHOPS, PT I
Keywords
Field
DocType
Quantification, Micro-expression, Dynamic information, Constraint Local Model (CLM), Local Binary Pattern (LBP)
Computer vision,Expression (mathematics),Pattern recognition,Computer science,Local binary patterns,Psychological research,Feature extraction,Coding (social sciences),Artificial intelligence
Conference
Volume
ISSN
Citations 
8925
0302-9743
6
PageRank 
References 
Authors
0.44
11
5
Name
Order
Citations
PageRank
Wen-Jing Yan12659.43
Sujing Wang269037.65
Yu-Hsin Chen383637.69
Guoying Zhao43767166.92
Xiaolan Fu578660.72