Face feature extraction and
classifier designing are two essential parts for face recognition.
For years, we have been working on methods including discriminant
subspace analysis, Face Specific Subspace (FSS), Adaboosted Gabor
Fisher Classifier (AGFC), Support Vectors based Kernel discriminant
analysis (SV-KFDA), Local Gabor Binary Patterns (LGBP), Local Gabor
Phase Pattern (LGPP).
Abstract:
We present an individual appearance model based method, named
Face-Specific Subspace (FSS), for recognizing human faces under
variation in lighting, expression and viewpoint. This method is
motivated, but essentially different from the traditional Eigenface.
In Eigenface, each face image is represented as a point in a low
dimensional face subspace shared by all faces; however, we
experimentally show that one of the demerits of such a strategy is
that the most discriminant features of a specific face are not
accurately represented. Therefore, we propose to model each face by
one individual face subspace, named Face-Specific Subspace. Distance
from the face-specific subspace, that is, the reconstruction error,
is then exploited as the similarity measurement for identification.
Furthermore, to enable the proposed approach to solve the single
example problem, a technique to derive multi-samples from one single
example is further developed. Extensive experiments on several
academic databases show that our method significantly outperforms
Eigenface and template matching, which intensively indicate its
robustness under variation in illumination, expression and
viewpoint. See
our IJIST2003 paper
or contact Dr.Shiguang Shan.
Abstract:
Face
representation based on Gabor features has attracted much attention
and achieved great success in face recognition area for the
advantages of the Gabor features. However, Gabor features currently
adopted by most systems are redundant and too high dimensional. In
this paper, we propose a face recognition method using AdaBoosted
Gabor features, which are not only low dimensional but also
discriminant. The main contribution of the paper lies in two points:
(1) AdaBoost is successfully applied to face recognition by
introducing the intra-face and extra-face difference space in the
Gabor feature space; (2) An appropriate re-sampling scheme is
adopted to deal with the imbalance between the amount of the
positive samples and that of the negative samples. By using the
proposed method, only hundreds of Gabor features are selected.
Experiments on FERET database have shown that these hundreds of
Gabor features are enough to achieve good performance comparable to
that of methods using the complete set of Gabor features. See
our FG2004
paper
or contact Dr.Shiguang Shan.
Abstract:
Gabor features have been recognized as one of the most successful
face representations, but it is too high dimensional for fast
extraction and accurate classification. we have proposed the
AdaBoost Gabor Fisher Classifier (AGFC) for robust face recognition,
in which a chain AdaBoost learning method based on Bootstrap
re-sampling is proposed and applied to face recognition with
impressive recognition performance. In AGFC, AdaBoost is exploited
to select optimally the most informative Gabor features (hereinafter
as AdaGabor features). The selected low-dimensional AdaGabor
features are then classified by Fisher discriminant analysis for
final face identification. Our experiments on two large-scale face
databases, FERET and CAS-PEAL (with 5789 images of 1040 subjects),
have shown that the proposed method can effectively reduce the
dimensionality of Gabor features and greatly increase the
recognition accuracy. In addition, our experimental results show its
robustness to variations in facial expression and accessories. See
our AMFG2005 paper
or contact Dr.Shiguang Shan.
Abstract:
We
have proposed a novel nonlinear discriminant analysis method named
by Kernerlized Maximum Average Margin Criterion (KMAMC), which has
combined the idea of Support Vector Machine with the Kernel Fisher
Discriminant Analysis (KFD). We also use a simple method to prove
the relationship between both kernel methods. The difference of
KMAMC from traditional KFD methods include (1) the within-class and
between-class scatter matrices are computed based on the support
vectors instead of all the samples; (2) multiple centers are
exploited instead of the single center in computing the two scatter
matrix; (3) the discriminant criteria is formulated as subtracting
the trace of within-class scatter matrix from that of the
between-class scatter matrix, therefore, the tedious singularity
problem is avoided. These features have made KMAMC more practical
for real-world applications. Our experiments on two face databases,
the FERET and CAS-PEAL face database, have illustrated its excellent
performance compared with some traditional methods such as Eigenface,
Fisherface, and KFD. See our
CVPR2005 paper
or contact Mr.Baochang
Zhang.
Abstract:
For years,
researchers in face recognition area have been representing and
recognizing faces based on subspace discriminant analysis or
statistical learning. Nevertheless, these approaches are always
suffering from the generalizability problem. we have proposed a
novel non-statistics based face representation approach, Local Gabor
Binary Pattern Histogram Sequence (LGBPHS), in which training
procedure is unnecessary to construct the face model, so that the
generalizability problem is naturally avoided. In this approach, a
face image is modeled as a “histogram sequence” by concatenating the
histograms of all the local regions of all the local Gabor magnitude
binary pattern maps. For recognition, histogram intersection is used
to measure the similarity of different LGBPHSes and the nearest
neighborhood is exploited for final classification. Additionally, we
have further proposed to assign different weights for each histogram
piece when measuring two LGBPHSes. Our experimental results on AR
and FERET face database show the validity of the proposed approach
especially for partially occluded face images, and more
impressively, we have achieved the best result on FERET face
database. See our
ICCV2005 paper
or contact Mr.Wenchao Zhang.
Abstract:
Gabor
features has been recognized as one of the most successful
representation methods, such as Elastic Graph Matching, Gabor Fisher
Classifier, and AdaBoost Gabor Fisher Classifier. One of the key
issues in using Gabor features is how to efficiently reduce its high
dimensionality. This paper proposes a multiple Fisher classifiers
combination approach based on re-grouping Gabor features selected by
using re-sampling and AdaBoost. At least two advantages can be
observed with the proposed method: (1) more discriminative Gabor
features are exploited in distributed Fisher classifiers by
re-grouping the selected Gabor features; (2) combination of multiple
Fisher classifiers improves the final performance of the
classification compared with the traditional Fisher classifier. Our
extensive experiments on two large face databases, FERET and
CAS-PEAL, have impressively shown the effectiveness of the proposed
method. See our
BMVC2005 paper or contact
Mr.Yu Su.
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