TWO-LEVEL POSE
ESTIMATION USING MAJORITY VOTING OF GABOR WAVELETS AND
BUNCH GRAPH ANALYSIS A
two-level approach for estimating face pose from a
single static image is presented. Gabor wavelets are
used as the basic features. The objective of the first
level is to derive a good estimate of the pose within
some uncertainty. The objective of the second level
processing is to minimize this uncertainty by analyzing
finer structural details captured by the bunch graphs.
The first level analysis enables the use of rigid bunch
graph. The framework is evaluated with extensive series
of experiments. Using only a single level, 90% accuracy
(within 30 degrees) was achieved on the complete dataset
of 1,395 images. Second level classification was
evaluated for three sets of poses with accuracies
ranging between 67%-73% without any uncertainty.
We present a two-level classification
framework for the accurate pose determination, so as to
determine the face pointing direction. The two-level
approach is based upon the rationale that visual cues
characterizing facial pose has unique multi-resolutions
partial frequency and structural signatures. The first
level of the approach has the objective of deriving pose
estimates with some uncertainty. First level output
confines the poses in a small range so that rigid bunch
graph can be used thereafter. The objective of the
second level processing is to minimize this uncertainty
by systematically analyzing the finer structural details
captured by the bunch graphs. Gabor wavelets are used as
the features. In the coarse level, every Gabor wavelet
response is classified using the subspace projection.
Two different subspaces are used to get the best
descriptors, which are PCA and Kernel Discriminant
Analysis (KDA). The classification results from
different Gabor wavelet are combined by majority voting.
The first level localizes the poses up to an NxN (N=3)
sub-window around the true poses. In the fine level, the
pose estimation refined by using rigid bunch graph
matching, which utilizes the geometrical details of the
salient facial component.
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