Detecting micro-expression intensity changes from videos based on hybrid deep CNN Thuseethan, Selvarajah, Rajasegarar, Sutharshan and Yearwood, John Leighton 2019, Detecting micro-expression intensity changes from videos based on hybrid deep CNN, in PAKDD 2019 : Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Cham, Switzerland, pp. 387-399, doi: 10.1007/978-3-030-16142-2_30. Attached Files Name Description MIMEType Size Downloads Title Detecting micro-expression intensity changes from videos based on hybrid deep CNN Author(s) Thuseethan, Selvarajah Rajasegarar, Sutharshan orcid.org/0000-0002-6559-6736 Yearwood, John Leighton orcid.org/0000-0002-7562-6767 Conference name Knowledge Discovery and Data Mining. Conference (23rd : 2019 : Macau, China) Conference location Macau, China Conference dates 2019/04/14 - 2019/04/17 Title of proceedings PAKDD 2019 : Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining Editor(s) Yang, Qiang Zhou, Zhi-Hua Gong, Zhiguo Zhang, Min-Ling Huang, Sheng-Jun Publication date 2019 Series Knowledge Discovery and Data Mining Conference Start page 387 End page 399 Total pages 13 Publisher Springer Place of publication Cham, Switzerland Keyword(s) Micro-expression intensity Convolutional Neural Networks Hybrid framework Fusion mechanism ISBN 9783030161415 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-030-16142-2_30 Field of Research 08 Information and Computing Sciences HERDC Research category E1 Full written paper - refereed Copyright notice ©2019, Springer Nature Switzerland AG Persistent URL http://hdl.handle.net/10536/DRO/DU:30121970 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment Centre for Pattern Recognition and Data Analytics (PRADA) Related Links Link Description Connect to Elements publication management system Go to link with your DU access privileges   Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 20 May 2019, 13:47:31 EST Tue, 21 May 2019, 05:01:19 EST Tue, 21 May 2019, 06:04:59 EST Wed, 22 May 2019, 11:13:20 EST Wed, 22 May 2019, 11:15:29 EST Wed, 22 May 2019, 11:16:32 EST Wed, 22 May 2019, 12:02:45 EST Sat, 04 Dec 2021, 01:32:21 EST Filtered Full Citation counts:   Cited 5 times in TR Web of Science Cited 6 times in Scopus Search Google Scholar Access Statistics: 374 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Mon, 20 May 2019, 13:47:31 EST