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<div dir="ltr" class="">[Apologies if you receive multiple copies of this CFP] <br class="">
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<div dir="ltr" class=""><span style="font-size: 12.8px;" class=""><br class="">
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<div dir="ltr" class=""><span style="font-size: 12.8px;" class=""><span class="gmail-il">CALL</span> FOR <span class="gmail-il">PAPERS</span>: </span>
<div class=""><b class=""><font size="4" class="">2nd Facial Micro-Expression Grand Challenge (<span class="gmail-il">MEGC</span>) Workshop</font></b></div>
<div class=""><span style="font-size: 12.8px;" class="">in conjunction with the <b class="">14th IEEE Conference on Automatic Face and Gesture Recognition (FG) 2019</b></span></div>
<div class=""><span style="font-size: 12.8px;" class="">- Lille, France, 14-18 May 2019.</span></div>
<div class=""><br style="font-size: 12.8px;" class="">
<span style="font-size: 12.8px;" class=""><a href="https://facial-micro-expressiongc.github.io/MEGC2019/" class="">https://facial-micro-expressiongc.github.io/MEGC2019/</a></span></div>
<div class=""><span style="font-size: 12.8px;" class=""><br class="">
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<div class="">Facial micro-expressions (MEs) are involuntary movements of the face that occur spontaneously when a person experiences an emotion but attempts to suppress or repress the facial expression, typically found in a high-stakes environment. As such,
the duration of MEs is very short with the general duration of not more than 500 milliseconds (ms), and is the telltale sign that distinguishes them from a normal facial expression. Computational analysis and automation of tasks on micro-expressions is an
emerging area in face research, with a strong interest appearing as recent as 2014. Only recently, the availability of a few spontaneously induced facial micro-expression datasets has provided the impetus to advance further from the computational aspect. Particularly
comprehensive are two state-of-the-art FACS coded datasets: the Chinese Academy of Sciences Micro-Expression Database II (CASME II) with 247 MFEs at 200 fps and the Spontaneous Facial Micro-Movement Dataset (SAMM) with 159 MFEs at 200 fps. In addition, there
is recent interest in acquiring “in-the-wild” datasets to further introduce real-world scenarios. While much research has been done on these datasets individually, there have been little attempts to introduce a more rigorous and realistic evaluation to work
done in this domain. This is the second edition of this workshop, which aims to promote interactions between researchers and scholars not only from within this niche area of facial micro-expression research, but also including those from broader, general areas
of expression and psychology research.<br class="">
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<div class=""><span style="font-size: 12.8px;" class="">This workshop has three main agendas:</span></div>
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<div class=""><span style="font-size: 12.8px;" class="">1. To organize, for the first time, a <b class="">Micro-Expression Spotting Challenge</b> based on CAS(ME)^2 and SAMM datasets.</span></div>
<div class=""><span style="font-size: 12.8px;" class="">2. To organize the second <b class="">Micro-Expression Cross-DB Recognition Challenge,</b> which increases its previous coverage of CASME II and SAMM datasets to include the SMIC dataset.</span></div>
<div class=""><span style="font-size: 12.8px;" class="">3.To solicit original works that addresses a variety of challenges in ME research, but not limited to:</span></div>
<div class=""><span style="font-size: 12.8px;" class=""><br class="">
</span></div>
<div class=""><span style="font-size: 12.8px;" class="">- ME Spotting / Detection</span></div>
<div class=""><span style="font-size: 12.8px;" class="">- ME Recognition</span></div>
<div class=""><span style="font-size: 12.8px;" class="">- ME Feature Representations & Computational Analysis</span></div>
<div class=""><span style="font-size: 12.8px;" class="">- Unified ME Spot-and-Recognize schemes</span></div>
<div class=""><span style="font-size: 12.8px;" class="">- Deep Learning Techniques for ME Analysis</span></div>
<div class=""><span style="font-size: 12.8px;" class="">- ME Data Synthesis</span></div>
<div class=""><span style="font-size: 12.8px;" class="">- New ME Datasets</span></div>
<div class=""><span style="font-size: 12.8px;" class="">- Real-world Applications of ME</span></div>
<div class=""><span style="font-size: 12.8px;" class="">- Computational Linkages to Psychology / Neuroscience in MEs</span></div>
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<div class=""><br style="font-size: 12.8px;" class="">
<span style="font-size: 12.8px;" class="">SUBMISSIONS</span> <br style="font-size: 12.8px;" class="">
<span style="font-size: 12.8px;" class="">Submission site is open, and accessible at:</span><br style="font-size: 12.8px;" class="">
<font color="#1155cc" class=""><span style="font-size: 12.8px;" class=""><u class=""><a href="https://cmt3.research.microsoft.com/MEGC2019" target="_blank" class="">https://cmt3.research.microsoft.com/MEGC2019</a></u></span></font></div>
<div class=""><font color="#1155cc" class=""><u class=""><br style="font-size: 12.8px;" class="">
</u></font><span style="font-size: 12.8px;" class="">Each paper will be reviewed by at least two reviewers from the TPC or external experts </span><span style="font-size: 12.8px;" class="">in "double-blind" fashion, and so the submitted version of the paper
should be appropriately anonymized not to reveal the authors or authors’ institutions. The submitted <span class="gmail-il">papers</span> </span><span style="font-size: 12.8px;" class="">should present original work, not currently </span><span style="font-size: 12.8px;" class="">under
review elsewhere and </span><span style="font-size: 12.8px;" class="">should have no substantial overlap with already published work. </span>All challenge entries should be accompanied by a paper submission.</div>
<div class=""><br class="">
<span style="font-size: 12.8px;" class="">Submissions (in PDF) can either be in the form of a short paper (max 4 pages + 1 page for </span><span style="font-size: 12.8px;" class="">reference) </span><span style="font-size: 12.8px;" class="">or </span><span style="font-size: 12.8px;" class="">long
paper </span><span style="font-size: 12.8px;" class="">(no more than 8 pages including references) i</span><span style="font-size: 12.8px;" class="">n IEEE </span><span class="gmail-m_5622539067052612886gmail-il" style="font-size: 12.8px;">FG</span><span style="font-size: 12.8px;" class=""> 2019
paper format. <a href="http://fg2019.org/participate/regular-papers/" class="">See here</a> for further instructions. Workshop papers presented at FG 2019 will appear in the IEEE Xplore digital library. If a paper is accepted, it is assumed that an author
will register and attend the workshop to present the paper. (Papers that are not presented will not be published in the proceedings.)</span></div>
<div class=""><span style="font-size: 12.8px;" class=""><b class=""><br class="">
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<div class=""><span style="font-size: 12.8px;" class=""><b class="">Dates</b></span><br style="font-size: 12.8px;" class="">
<span style="font-size: 12.8px;" class=""><strike class="">27 January 2019 </strike> <b class="">4 February 2019</b> Extended Submission deadline</span><br style="font-size: 12.8px;" class="">
<span style="font-size: 12.8px;" class="">21 February 2019: Notification of acceptance</span><br style="font-size: 12.8px;" class="">
<span style="font-size: 12.8px;" class="">26 February 2019: Camera ready submission</span><br style="font-size: 12.8px;" class="">
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<div class=""><span style="font-size: 12.8px;" class=""><b class="">Organizing Chairs</b></span><br style="font-size: 12.8px;" class="">
<div class=""><span style="font-size: 12.8px;" class="">Moi Hoon Yap, Manchester Metropolitan University, UK </span></div>
<div class=""><span style="font-size: 12.8px;" class="">Sujing Wang, Chinese Academy of Sciences, China</span></div>
<div class=""><span style="font-size: 12.8px;" class="">John See, Multimedia University, Malaysia</span></div>
<div class=""><span style="font-size: 12.8px;" class="">Xiaopeng Hong, University of Oulu, Finland</span></div>
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<b class="">Advisory Panel<br class="">
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<div class="">Xiaolan Fu, Chinese Academy of Sciences, China </div>
<div class="">Guoying Zhao, University of Oulu, Finland</div>
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<div class="">For further enquries, please contact Moi Hoon Yap (<a href="mailto:m.yap@mmu.ac.uk" class="">m.yap@mmu.ac.uk</a>) or John See (<a href="mailto:johnsee@mmu.edu.my" class="">johnsee@mmu.edu.my</a>)</div>
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