CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation
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 previous...
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| Vydáno v: | PloS one Ročník 9; číslo 1; s. e86041 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
Public Library of Science
27.01.2014
Public Library of Science (PLoS) |
| Témata: | |
| ISSN: | 1932-6203, 1932-6203 |
| On-line přístup: | Získat plný text |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. 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 280x340 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. |
| Audience | Academic |
| Author | Zhao, Guoying Yan, Wen-Jing Li, Xiaobai Liu, Yong-Jin Wang, Su-Jing Fu, Xiaolan Chen, Yu-Hsin |
| AuthorAffiliation | 4 TNList, Department of Computer Science and Technology, Tsinghua University, Beijing, China 3 Center for Machine Vision Research, Department of Computer Science and Engineering, University of Oulu, Oulu, Finland 2 University of Chinese Academy of Sciences, Beijing, China University of Lincoln, United Kingdom 1 State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China |
| AuthorAffiliation_xml | – name: 2 University of Chinese Academy of Sciences, Beijing, China – name: 3 Center for Machine Vision Research, Department of Computer Science and Engineering, University of Oulu, Oulu, Finland – name: 4 TNList, Department of Computer Science and Technology, Tsinghua University, Beijing, China – name: 1 State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China – name: University of Lincoln, United Kingdom |
| Author_xml | – sequence: 1 givenname: Wen-Jing surname: Yan fullname: Yan, Wen-Jing – sequence: 2 givenname: Xiaobai surname: Li fullname: Li, Xiaobai – sequence: 3 givenname: Su-Jing surname: Wang fullname: Wang, Su-Jing – sequence: 4 givenname: Guoying surname: Zhao fullname: Zhao, Guoying – sequence: 5 givenname: Yong-Jin surname: Liu fullname: Liu, Yong-Jin – sequence: 6 givenname: Yu-Hsin surname: Chen fullname: Chen, Yu-Hsin – sequence: 7 givenname: Xiaolan surname: Fu fullname: Fu, Xiaolan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24475068$$D View this record in MEDLINE/PubMed |
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| Copyright | COPYRIGHT 2014 Public Library of Science 2014 Yan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2014 Yan et al 2014 Yan et al |
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| References_xml | – year: 2010 ident: ref5 article-title: Microexpression spotting in video using optical strain: University of South Florida – volume: 19 start-page: 508 year: 2008 ident: ref2 article-title: Reading Between the Lies: Identifying Concealed and Falsified Emotions in Universal Facial Expressions publication-title: Psychol Sci doi: 10.1111/j.1467-9280.2008.02116.x – ident: ref10 doi: 10.1007/978-3-642-24571-8_16 – volume: 61 start-page: 38 year: 1995 ident: ref25 article-title: Active shape models-their training and application publication-title: Comput Vis Image Underst doi: 10.1006/cviu.1995.1004 – ident: ref9 doi: 10.1049/ic.2009.0244 – ident: ref23 doi: 10.1109/FG.2013.6553799 – ident: ref12 doi: 10.1109/FG.2013.6553709 – ident: ref24 – volume: 29 start-page: 915 year: 2007 ident: ref28 article-title: Dynamic texture recognition using local binary patterns with an application to facial expressions publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2007.1110 – ident: ref8 doi: 10.1109/ICCV.2011.6126401 – ident: ref13 doi: 10.1109/CVPRW.2010.5543262 – volume: 29 start-page: 288 year: 1974 ident: ref20 article-title: Detecting deception from the body or face publication-title: J Pers Soc Psychol doi: 10.1037/h0036006 – year: 2013 ident: ref3 article-title: How Fast are the Leaked Facial Expressions: The Duration of Micro-Expressions publication-title: J Nonverbal Behav – ident: ref15 – ident: ref16 doi: 10.1109/AFGR.1998.670949 – volume: 28 start-page: 807 year: 2010 ident: ref17 article-title: Multi-pie publication-title: Image and Vision Computing doi: 10.1016/j.imavis.2009.08.002 – ident: ref4 – ident: ref22 doi: 10.1109/FG.2013.6553717 – ident: ref19 doi: 10.1109/AFGR.2008.4813324 – volume: 6 start-page: 255 year: 1988 ident: ref26 article-title: Image registration by local approximation methods publication-title: Image Vis Comput doi: 10.1016/0262-8856(88)90016-9 – volume: 24 start-page: 971 year: 2002 ident: ref27 article-title: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2002.1017623 – volume: 35 start-page: 181 year: 2011 ident: ref6 article-title: Evidence for training the ability to read microexpressions of emotion publication-title: Motiv Emot doi: 10.1007/s11031-011-9212-2 – year: 2013 ident: ref11 article-title: Face Recognition and Micro-expression Recognition Based on Discriminant Tensor Subspace Analysis Plus Extreme Learning Machine publication-title: Neural Processing Letters – ident: ref18 – volume: 32 start-page: 88 year: 1969 ident: ref1 article-title: Nonverbal leakage and clues to deception publication-title: Psychiatry doi: 10.1080/00332747.1969.11023575 – ident: ref7 doi: 10.1109/FG.2011.5771451 – volume: 72 start-page: 1429 year: 1997 ident: ref21 article-title: The ability to detect deceit generalizes across different types of high-stake lies publication-title: J Pers Soc Psychol doi: 10.1037/0022-3514.72.6.1429 – ident: ref14 – reference: 18466413 - Psychol Sci. 2008 May;19(5):508-14 – reference: 5779090 - Psychiatry. 1969 Feb;32(1):88-106 – reference: 17431293 - IEEE Trans Pattern Anal Mach Intell. 2007 Jun;29(6):915-28 – reference: 20490373 - Proc Int Conf Autom Face Gesture Recognit. 2010 May 1;28(5):807-813 – reference: 9177024 - J Pers Soc Psychol. 1997 Jun;72(6):1429-39 |
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| SubjectTerms | Algorithms Biometry - methods Brain research Computer Science Data bases Databases, Factual Datasets Emotions Emotions - physiology Engineering Evaluation Face - anatomy & histology Face - physiology Facial Expression Feature extraction Forensic Sciences - methods Humans Information Storage and Retrieval - methods International conferences Interrogation Laboratories Light Pattern Recognition, Automated - methods Police Questioning Researchers Reviews Social and Behavioral Sciences Spatial discrimination Spatial resolution Support Vector Machine Temporal resolution Vision systems |
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| Title | CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation |
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| Volume | 9 |
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