2,228 research outputs found
Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis \& Application
Spontaneous subtle emotions are expressed through micro-expressions, which
are tiny, sudden and short-lived dynamics of facial muscles; thus poses a great
challenge for visual recognition. The abrupt but significant dynamics for the
recognition task are temporally sparse while the rest, irrelevant dynamics, are
temporally redundant. In this work, we analyze and enforce sparsity constrains
to learn significant temporal and spectral structures while eliminate
irrelevant facial dynamics of micro-expressions, which would ease the challenge
in the visual recognition of spontaneous subtle emotions. The hypothesis is
confirmed through experimental results of automatic spontaneous subtle emotion
recognition with several sparsity levels on CASME II and SMIC, the only two
publicly available spontaneous subtle emotion databases. The overall
performances of the automatic subtle emotion recognition are boosted when only
significant dynamics are preserved from the original sequences.Comment: IEEE Transaction of Affective Computing (2016
Multi-task CNN Model for Attribute Prediction
This paper proposes a joint multi-task learning algorithm to better predict
attributes in images using deep convolutional neural networks (CNN). We
consider learning binary semantic attributes through a multi-task CNN model,
where each CNN will predict one binary attribute. The multi-task learning
allows CNN models to simultaneously share visual knowledge among different
attribute categories. Each CNN will generate attribute-specific feature
representations, and then we apply multi-task learning on the features to
predict their attributes. In our multi-task framework, we propose a method to
decompose the overall model's parameters into a latent task matrix and
combination matrix. Furthermore, under-sampled classifiers can leverage shared
statistics from other classifiers to improve their performance. Natural
grouping of attributes is applied such that attributes in the same group are
encouraged to share more knowledge. Meanwhile, attributes in different groups
will generally compete with each other, and consequently share less knowledge.
We show the effectiveness of our method on two popular attribute datasets.Comment: 11 pages, 3 figures, ieee transaction pape
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