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Random walk-based feature learning for micro-expression recognition
Jain Deepak Kumar1,2; Zhang Zhang1,2; Huang Kaiqi1,2
Source PublicationPattern Recognition Letters
2018
IssueXXPages:1-10
Abstract
Facial expression recognition (FER) and its analysis becomes an attractive research study in the fields of computer vision applications and pattern recognition. These facial expressions are generally categorized into two kinds such as micro and macro-expressions. To detect the macro-expression effectively, an angle based pattern extraction models and Markov models are employed. But the micro- expression delivers more detailed information than the macro-expression. Other diffculties such as short durations and rapid spontaneous facial expression are induced due to the detection and analysis of the micro-expression. To solve these challenges, we propose the three novel techniques such as Active Shape Modeling (ACM), Random Walk (RW) and the Artificial Neural Network (ANN) which helps to improve the overall performance effectively. The key points from the facial expression over the video frames are predicted using ASM and are spatially associated with the original face through the procrustes analysis. Then the RW algorithm is used to learn the training features prior to ANN model. This RW is integrated with ANN model to improve the learning performance of micro-expression with minimum computation complexity. The experimental validation on two spontaneous micro-expression datasets such as Chinese Academy of Sciences Micro-Expression (CASME) and Spontaneous Micro-expression (SMIC) over the existing SVM classifiers shows its effectiveness in automatic micro– expression learning applications.
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Facial expression recognition (FER) and its analysis becomes an attractive research study in the fields of computer vision applications and pattern recognition. These facial expressions are generally categorized into two kinds such as micro and macro-expressions. To detect the macro-expression effectively, an angle based pattern extraction models and Markov models are employed. But the micro- expression delivers more detailed information than the macro-expression. Other diffculties such as short durations and rapid spontaneous facial expression are induced due to the detection and analysis of the micro-expression. To solve these challenges, we propose the three novel techniques such as Active Shape Modeling (ACM), Random Walk (RW) and the Artificial Neural Network (ANN) which helps to improve the overall performance effectively. The key points from the facial expression over the video frames are predicted using ASM and are spatially associated with the original face through the procrustes analysis. Then the RW algorithm is used to learn the training features prior to ANN model. This RW is integrated with ANN model to improve the learning performance of micro-expression with minimum computation complexity. The experimental validation on two spontaneous micro-expression datasets such as Chinese Academy of Sciences Micro-Expression (CASME) and Spontaneous Micro-expression (SMIC) over the existing SVM classifiers shows its effectiveness in automatic micro– expression learning applications.
KeywordMicro Expression Random Walk
DOI10.1016/j.patrec.2018.02.004
Indexed BySCI
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Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21196
Collection智能感知与计算研究中心
Corresponding AuthorHuang Kaiqi
Affiliation1.CRIPAC & NLPR, CASIA, PR China
2.University of Chinese Academy of Sciences,Beijing,China
Recommended Citation
GB/T 7714
Jain Deepak Kumar,Zhang Zhang,Huang Kaiqi. Random walk-based feature learning for micro-expression recognition[J]. Pattern Recognition Letters,2018(XX):1-10.
APA Jain Deepak Kumar,Zhang Zhang,&Huang Kaiqi.(2018).Random walk-based feature learning for micro-expression recognition.Pattern Recognition Letters(XX),1-10.
MLA Jain Deepak Kumar,et al."Random walk-based feature learning for micro-expression recognition".Pattern Recognition Letters .XX(2018):1-10.
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