Abstract book (pdf) - ICPR 2010

15:00-17:10, Paper MoBT9.5
Lipreading: A Graph Embedding Approach
Zhou, Ziheng, Univ. of Oulu
Zhao, Guoying, Univ. of Oulu
Pietikäinen, Matti, Univ. of Oulu
In this paper, we propose a novel graph embedding method for the problem of lipreading. To characterize the temporal
connections among video frames of the same utterance, a new distance metric is defined on a pair of frames and graphs
are constructed to represent the video dynamics based on the distances between frames. Audio information is used to assist
in calculating such distances. For each utterance, a subspace of the visual feature space is learned from a well-defined intrinsic
and penalty graph within a graph-embedding framework. Video dynamics are found to be well preserved along
some dimensions of the subspace. Discriminatory cues are then decoded from curves of the projected visual features to
classify different utterances.
15:00-17:10, Paper MoBT9.6
Face Recognition using a Multi-Manifold Discriminant Analysis Method
Yang, Wankou, Southeast Univ. Nanjing
Sun, Changyin, Southeast Univ. Nanjing
Zhang, Lei, The Hong Kong Pol. Univ.
In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for face feature extraction and face
recognition, which is based on graph embedded learning and under the Fisher discirminant analysis framework. In MMDA,
the within-class graph and between-class graph are designed to characterize the within-class compactness and the between-class
separability, respectively, seeking for the discriminant matrix that simultaneously maximizing the betweenclass
scatter and minimizing the within-class scatter. In addition, the within-class graph can also represent the sub-manifold
information and the between-class graph can also represent the multi-manifold information. The proposed MMDA is examined
by using the FERET face database, and the experimental results demonstrate that MMDA works well in feature
extraction and lead to good recognition performance.
15:00-17:10, Paper MoBT9.7
Globally-Preserving based Locally Linear Embedding
Hui, Kanghua, Chinese Acad. of Sciences
Wang, Chunheng, Chinese Acad. of Sciences
Xiao, Baihua, Chinese Acad. of Sciences
The locally linear embedding (LLE) algorithm is considered as a powerful method for the problem of nonlinear dimensionality
reduction. In this paper, a new method called globally-preserving based LLE (GPLLE) is proposed. It not only
preserves the local neighborhood, but also keeps those distant samples still far away, which solves the problem that LLE
may encounter, i.e. LLE only makes local neighborhood preserving, but cannot prevent the distant samples from nearing.
Moreover, GPLLE can estimate the intrinsic dimensionality d of the manifold structure. The experiment results show that
GPLLE always achieves better classification performances than LLE based on the estimated d.
15:00-17:10, Paper MoBT9.8
3d Human Pose Estimation by an Annealed Two-Stage Inference Method
Wang, Yuan-Kai, Fu Jen Univ.
Cheng, Kuang-You, Fu Jen Univ.
This paper proposes a novel human motion capture method that locates human body joint position and reconstructs the
human pose in 3D space from monocular images. We propose a two-stage framework including 2D and 3D probabilistic
graphical models which can solve the occlusion problem for the estimation of human joint positions. The 2D and 3D
models adopt directed acyclic structure to avoid error propagation of inference in the models. Both the 2D and 3D models
utilize the Expectation Maximization algorithm to learn prior distributions of the models. An annealed Gibbs sampling
method is proposed for the two-stage method to inference the maximum posteriori distributions of joint positions. The annealing
process can efficiently explore the mode of distributions and find solutions in high-dimensional space. Experiments
are conducted on the Human Eva dataset to show the effectiveness of the proposed method. The experimental data are
image sequences of walking motion with a full 180 turn around a region, which causes occlusion of poses and loss of
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