PSYCH OpenIR  > 认知与发展心理学研究室
Alternative TitleMicro-expression: the Characteristics and Automatic Recognition
Other AbstractMicro-expression is a leaked fast facial expression which may present when people try to conceal their genuine emotions. Micro-expression provide a new perspective to understand human being’s emotion since it reveals individuals true intent. Therefore, micro-expression may be used as a cue in the fields of security, clinic and police interrogation. However, there are three problems remains to be resolved. First, the characteristics of micro-expression is not clear, such as the duration and the action pattern. Second, there is no efficient and effective analysis tool that can quantify micro-expression in details. Third, without fine understandings of the characteristics of micro-expression and without a spontaneous micro-expression database, training computer to automatically recognize micro-expression is difficult.
With these three problems, this study conductedan inter-disciplinary research between psychology and computer science. First, weeliciteda certain quantity of fast leaked facial expressions and fit a curve to the distribution. We found the upper limit duration of micro-expression is around 500 ms, while the upper limit onset duration is around 260 ms. Micro-expression usually have partial action and the intensity is relatively low.In addition, the classification of micro-expression seems different from the ordinary facial expressions.This work describe the basic characteristics of micro-expression and provide evidence for defining it.To further quantify the micro-expressions and enhance theefficiency,we appliedConstrained Local Model(CLM) and Local Binary Pattern(LBP) to analyzing micro-expression, including the moving distance, velocity, angle and texture change. There was no difference between these two methods with manual coding in spotting the apex frames from 50 micro-expression samples. This work helpsto analysis micro-expression at a deeper level and tested the effectiveness of the feature extraction method. Second, we build a spontaneous micro-expression database CASME which contains 195 samples. Further, CASMEⅡwasbuild which contains 256 spontaneous micro-expressions, which improved frame rate, resolution and illumination. Third, based on the psychological study, the micro-expression database and the appropriate feature extraction methods selected, we usedLBP-TOP to recognize micro-expression. Further, we used Robust Principle Component Analysis (RPCA) to remove the redundancy and then used Local Spatio-temporal Directional(LSTD) to recognize micro-expression.
Subject Area基础心理学
Keyword微表情表达 微表情识别 数据库 特征提取
Degree Discipline心理学
Degree Grantor中国科学院研究生院
Place of Conferral北京
Document Type学位论文
Recommended Citation
GB/T 7714
颜文靖. 微表情的表达特点与自动识别研究[D]. 北京. 中国科学院研究生院,2014.
Files in This Item:
File Name/Size DocType Version Access License
颜文靖-博士x学位论文.pdf(3963KB)学位论文 限制开放CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[颜文靖]'s Articles
Baidu academic
Similar articles in Baidu academic
[颜文靖]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[颜文靖]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.