[公告] 博士論文研究計畫口試：蘇德峰(2014/06/09)

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�D�@�@�ءG Multi-Attribute Sparse Coding Technique for
Action Classification and Facial Expression Recognition

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Finding a��good��representation of a signal in which the structures,
patterns and dependencies of that signal are made more explicit has been the
topic of an enormous amount of recent research. In recent years, the sparse
coding technique attracts more and more attention because of its
effectiveness in extracting global properties from signals. Furthermore, the
sparse representation was designed to produce sparse solution at the group
level by considering group structure of training images. However, distinctive
objects or different action videos usually contain multiple data attributes
which are high-level descriptions about the properties of objects or actions.
For the action recognition problem, action video may contain multiple
attributes, such as different types of viewing angle, pose and illumination.
Such multi-attribute properties cannot be fully exploited by the group lasso
method since it is not designed to handle multiple attributes.
In this thesis, we propose multi-attribute sparse representation based method
enforced with group constraint for the action recognition and facial
expression recognition problems which contain multiple data attributes. For
the action recognition problem, an over-segmentation based background
modeling and foreground detection approach is employed to extract silhouettes
from action videos firstly. Then, multiple time intervals of the motion
history images are computed to capture motion and pose information in human
activities. Actions with multiple attributes can be represented by individual
attribute matrices to describe group property for each action instance. These
attribute matrices are incorporated into the formulation of l_1-minimization.
The sparsity property as well as the group constraints make the basis
selection in sparse coding more efficient in term of accuracy. Especially,
our approach is able to operate under the condition of partially labeled
attributes in the training data.
Furthermore, we integrate action units (AUs) information and multi-attribute
sparse coding for facial expression recognition. AUs not only can be
represented by an individual attribute mask to describe group property for
each facial expression video, but also as a constraint to enforce that the
same facial expressions should have very similar AUs. The group constraint
makes the basis selection in sparse coding more efficient and the AU
similarity constraint penalizes selecting the dictionary atoms with distance
far away the target instance. These groups constraint and the AU similarity
constraint are incorporated into the formulation of l_1-minimization to
recognize facial expression.
We will demonstrate the proposed multi-attribute sparse coding based method
through experiments on public multi-view human action datasets and facial
expression datasets to show the effectiveness and robustness of the proposed
method.

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