Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SAMM Long Videos: A Spontaneous Facial Micro- and Macro-Expressions
  Dataset   With the growth of popularity of facial micro-expressions in recent years,
the demand for long videos with micro- and macro-expressions remains high.
Extended from SAMM, a micro-expressions dataset released in 2016, this paper
presents SAMM Long Videos dataset for spontaneous micro- and macro-expressions
recognition and spotting. SAMM Long Videos dataset consists of 147 long videos
with 343 macro-expressions and 159 micro-expressions. The dataset is FACS-coded
with detailed Action Units (AUs). We compare our dataset with Chinese Academy
of Sciences Macro-Expressions and Micro-Expressions (CAS(ME)2) dataset, which
is the only available fully annotated dataset with micro- and
macro-expressions. Furthermore, we preprocess the long videos using OpenFace,
which includes face alignment and detection of facial AUs. We conduct facial
expression spotting using this dataset and compare it with the baseline of MEGC
III. Our spotting method outperformed the baseline result with F1-score of
0.3299.

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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ex
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
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The categories of this paper are: stat
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The categories of this paper are: adap-org
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The categories of this paper are: nlin
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The categories of this paper are: comp-gas
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The categories of this paper are: supr-con
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The categories of this paper are: eess
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The categories of this paper are: q-bio
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The categories of this paper are: math-ph
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-th
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The categories of this paper are: chem-ph