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Spotting micro-expressions on long videos sequences
Li, Jingting1; Soladié, Catherine1; Séguier, Renaud1; Wang, Su-Jing2; Yap, Moi Hoon3
2019
Conference Name14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019
Source PublicationProceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019
Conference DateMay 14, 2019 - May 18, 2019
Conference PlaceLille, France
PublisherInstitute of Electrical and Electronics Engineers Inc.
Contribution Rank2
AbstractThis paper presents two methods for the first Micro-Expression Spotting Challenge 2019 by evaluating local temporal pattern (LTP) and local binary pattern (LBP) on two most recent databases, i.e. SAMM and CAS(ME)2. First we propose LTP-ML method as the baseline results for the challenge and then we compare the results with the LBP-χ2-distance method. The LTP patterns are extracted by applying PCA in a temporal window on several facial local regions. The micro-expression sequences are then spotted by a local classification of LTP and a global fusion. The LBP-χ2-distance method is to compare the feature difference by calculating χ2distance of LBP in a time window, the facial movements are then detected with a threshold. The performance is evaluated by Leave-One-Subject-Out cross validation. The overlap frames are used to determine the True Positives and the metric F1-score is used to compare the spotting performance of the databases. The F1-score of LTP-ML result for SAMM and CAS(ME)2 are 0.0316 and 0.0179, respectively. The results show our proposed LTP-ML method outperformed LBP-χ2-distance method in terms of F1-score on both databases. © 2019 IEEE.
KeywordBaseline results - Cross validation - Facial movements - Feature differences - Local binary patterns - Micro-expressions - Temporal pattern - Temporal windows
Subject AreaGesture Recognition
DOI10.1109/FG.2019.8756626
ISBN9781728100890
Indexed ByEI
EI Accession Number20193307305590
EI KeywordsDatabase systems
EI Classification Number723.3 Database Systems
Citation statistics
Document Type会议论文
Identifierhttp://ir.psych.ac.cn/handle/311026/30035
Collection认知与发展心理学研究室
Affiliation1.CentraleSupélec, CNRS, IETR, UMR 6164, Rennes; F-35000, France;
2.Key Laboratory of Behavior Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing; 100101, China;
3.Manchester Metropolitan University, Manchester; M1 5GD, United Kingdom
Recommended Citation
GB/T 7714
Li, Jingting,Soladié, Catherine,Séguier, Renaud,et al. Spotting micro-expressions on long videos sequences[C]:Institute of Electrical and Electronics Engineers Inc.,2019.
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