50,195 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
Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables
Understanding nonlinear dynamical systems (NLDSs) is challenging in a variety
of engineering and scientific fields. Dynamic mode decomposition (DMD), which
is a numerical algorithm for the spectral analysis of Koopman operators, has
been attracting attention as a way of obtaining global modal descriptions of
NLDSs without requiring explicit prior knowledge. However, since existing DMD
algorithms are in principle formulated based on the concatenation of scalar
observables, it is not directly applicable to data with dependent structures
among observables, which take, for example, the form of a sequence of graphs.
In this paper, we formulate Koopman spectral analysis for NLDSs with structures
among observables and propose an estimation algorithm for this problem. This
method can extract and visualize the underlying low-dimensional global dynamics
of NLDSs with structures among observables from data, which can be useful in
understanding the underlying dynamics of such NLDSs. To this end, we first
formulate the problem of estimating spectra of the Koopman operator defined in
vector-valued reproducing kernel Hilbert spaces, and then develop an estimation
procedure for this problem by reformulating tensor-based DMD. As a special case
of our method, we propose the method named as Graph DMD, which is a numerical
algorithm for Koopman spectral analysis of graph dynamical systems, using a
sequence of adjacency matrices. We investigate the empirical performance of our
method by using synthetic and real-world data.Comment: 34 pages with 4 figures, Published in Neural Networks, 201
Mode Variational LSTM Robust to Unseen Modes of Variation: Application to Facial Expression Recognition
Spatio-temporal feature encoding is essential for encoding the dynamics in
video sequences. Recurrent neural networks, particularly long short-term memory
(LSTM) units, have been popular as an efficient tool for encoding
spatio-temporal features in sequences. In this work, we investigate the effect
of mode variations on the encoded spatio-temporal features using LSTMs. We show
that the LSTM retains information related to the mode variation in the
sequence, which is irrelevant to the task at hand (e.g. classification facial
expressions). Actually, the LSTM forget mechanism is not robust enough to mode
variations and preserves information that could negatively affect the encoded
spatio-temporal features. We propose the mode variational LSTM to encode
spatio-temporal features robust to unseen modes of variation. The mode
variational LSTM modifies the original LSTM structure by adding an additional
cell state that focuses on encoding the mode variation in the input sequence.
To efficiently regulate what features should be stored in the additional cell
state, additional gating functionality is also introduced. The effectiveness of
the proposed mode variational LSTM is verified using the facial expression
recognition task. Comparative experiments on publicly available datasets
verified that the proposed mode variational LSTM outperforms existing methods.
Moreover, a new dynamic facial expression dataset with different modes of
variation, including various modes like pose and illumination variations, was
collected to comprehensively evaluate the proposed mode variational LSTM.
Experimental results verified that the proposed mode variational LSTM encodes
spatio-temporal features robust to unseen modes of variation.Comment: Accepted in AAAI-1
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