031874f87fd7ad5d201e273fdf53c99a Ele_a2023v12n18p3947.pdf aadac8b8092094c0846172119d074197e469d4ba Ele_a2023v12n18p3947.pdf 82ac97340400c3b61cb048616794c7ee0a96a0775d9e41a11c33988437306beb Ele_a2023v12n18p3947.pdf Title: A Spatio-Temporal Spotting Network with Sliding Windows for Micro-Expression Detection Subject: Micro-expressions reveal underlying emotions and are widely applied in political psychology, lie detection, law enforcement and medical care. Micro-expression spotting aims to detect the temporal locations of facial expressions from video sequences and is a crucial task in micro-expression recognition. In this study, the problem of micro-expression spotting is formulated as micro-expression classification per frame. We propose an effective spotting model with sliding windows called the spatio-temporal spotting network. The method involves a sliding window detection mechanism, combines the spatial features from the local key frames and the global temporal features and performs micro-expression spotting. The experiments are conducted on the CAS(ME)2 database and the SAMM Long Videos database, and the results demonstrate that the proposed method outperforms the state-of-the-art method by 30.58% for the CAS(ME)2 and 23.98% for the SAMM Long Videos according to overall F-scores. Keywords: micro-expression spotting; sliding window; key frame extraction Author: Wenwen Fu, Zhihong An, Wendong Huang, Haoran Sun, Wenjuan Gong and Jordi Gonzàlez Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Tue Sep 19 10:55:50 2023 CEST ModDate: Tue Sep 19 11:01:45 2023 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 14 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 1887151 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- PMSYKQ+URWPalladioL-Bold Type 1 Custom yes yes no 10 0 INDVRJ+URWPalladioL-Roma Type 1 Custom yes yes no 15 0 ATZMDE+URWPalladioL-Ital Type 1 Custom yes yes no 19 0 GKSXYT+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 37 0 SOSTRQ+CMR10 Type 1 Builtin yes yes no 61 0 JFBPCW+CMSY10 Type 1 Builtin yes yes no 65 0 TOOWYB+CIDFont+F2 TrueType WinAnsi yes yes yes 73 0 FRWUAJ+CIDFont+F1 TrueType WinAnsi yes yes no 77 0 IOLAWT+PazoMath-Italic Type 1 Builtin yes yes no 95 0 LELDCK+PazoMath-BoldItalic Type 1 Builtin yes yes no 99 0 QDTWCG+MSBM10 Type 1 Builtin yes yes no 103 0 LLDMPC+CIDFont+F2 TrueType WinAnsi yes yes no 111 0 TOOWYB+CIDFont+F1 TrueType WinAnsi yes yes yes 114 0 PYJURH+CMEX10 Type 1 Builtin yes yes no 123 0 QQXUPP+PazoMath Type 1 Builtin yes yes no 127 0 BGDXOB+MSAM10 Type 1 Builtin yes yes no 134 0 TOOWYB+CIDFont+F1 TrueType WinAnsi yes yes yes 142 0 LCRPLP+CIDFont+F2 TrueType WinAnsi yes yes no 146 0 ZTYUZI+CIDFont+F1 TrueType WinAnsi yes yes no 158 0 ZDCZFZ+CIDFont+F2-GBK-EUC-H CID Type 0C Identity-H yes yes no 161 0 XCIHVZ+CIDFont+F1 TrueType WinAnsi yes yes no 199 0 KJMMEZ+CIDFont+F2-GBK-EUC-H CID Type 0C Identity-H yes yes no 202 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-03-23 02:34:27 CET RepresentationInformation: Ele_a2023v12n18p3947.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2024-03-22 15:46:37 CET Size: 1887151 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 409 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: The Spatio-Temporal Spotting Network with Sliding Windows Destination: section.2 Children: Item: Title: Spatial Information Extraction Destination: subsection.2.1 Item: Title: Key Frames Extraction Destination: subsection.2.2 Item: Title: Temporal Information Extraction Destination: subsection.2.3 Item: Title: Segment Merging Destination: subsection.2.4 Item: Title: Experiments Destination: section.3 Children: Item: Title: Datasets and Evaluation Metrics Destination: subsection.3.1 Item: Title: Experiments and Results Destination: subsection.3.2 Item: Title: Conclusions Destination: section.4 Item: Title: References Destination: section.5 Info: Title: A Spatio-Temporal Spotting Network with Sliding Windows for Micro-Expression Detection Author: Wenwen Fu, Zhihong An, Wendong Huang, Haoran Sun, Wenjuan Gong and Jordi Gonzàlez Subject: Micro-expressions reveal underlying emotions and are widely applied in political psychology, lie detection, law enforcement and medical care. Micro-expression spotting aims to detect the temporal locations of facial expressions from video sequences and is a crucial task in micro-expression recognition. In this study, the problem of micro-expression spotting is formulated as micro-expression classification per frame. We propose an effective spotting model with sliding windows called the spatio-temporal spotting network. The method involves a sliding window detection mechanism, combines the spatial features from the local key frames and the global temporal features and performs micro-expression spotting. The experiments are conducted on the CAS(ME)2 database and the SAMM Long Videos database, and the results demonstrate that the proposed method outperforms the state-of-the-art method by 30.58% for the CAS(ME)2 and 23.98% for the SAMM Long Videos according to overall F-scores. 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