Abstract book (pdf) - ICPR 2010

the idea of cross-dimensional comparison. This paper presents a novel face recognition approach that implements crossdimensional
comparison to solve the issue of pose invariance. Our approach implements a Gabor representation during
comparison to allow for variations in texture, illumination, expression and pose. Kernel scaling is used to reduce comparison
time during the branching search, which determines the facial pose of input images. The conducted experiments prove
the viability of this approach, with our larger kernel experiments returning 91.6% - 100% accuracy on a database comprised
of both local data, and data from the USF Human ID 3D database.
14:30-14:50, Paper ThBT5.4
Initialization and Pose Alignment in Active Shape Model
Xiong, Pengfei, Chinese Acad. of Sciences
Lei, Huang, Chinese Acad. of Sciences
Liu, Changping, Chinese Acad. of Sciences
In this paper, we propose a new algorithm for shape initialization and 3D pose alignment in Active Shape Model (ASM).
Instead of initializing with average shape in previous works, we build a scatter data interpolation model from key points
to obtain the initial shape, which ensures shape initialized around face organs. These key points are chosen from organs
of face shape and located with a strong classifier firstly. Then they are utilized to build a Radial Basis Function (RBF)
model to deform the average shape as initial shape. Besides, to cope with variety face poses, we define a 3D general shape
to align face shapes in 3D instead of 2D alignment in Classic ASM. With the accurate 3D rotation angles iteratively calculated
by Levenberg-Marquardt (LM) algorithm, shapes can be aligned to standard shape more reliably. Experiments
and comparisons on FERET show that both shape initialization and 3D pose alignment of our algorithm greatly improve
the location accuracy.
14:50-15:10, Paper ThBT5.5
3D Face Reconstruction using a Single or Multiple Views
Choi, Jongmoo, Univ. of Southern California
Medioni, Gerard, Univ. of Southern California
Lin, Yuping, Univ. of Southern California
Silva, Luciano, Univ. Federal do Parana
Bellon, Olga Regina Pereira, Univ. Federal do Parana
Pamplona, Mauricio, Univ. Federal do Parana
Faltemier, Timothy, Progeny Systems
We present a 3D face reconstruction system that takes as input either one single view or several different views. Given a
facial image, we first classify the facial pose into one of five predefined poses, then detect two anchor points that are then
used to detect a set of predefined facial landmarks. Based on these initial steps, for a single view we apply a warping
process using a generic 3D face model to build a 3D face. For multiple views, we apply sparse bundle adjustment to reconstruct
3D landmarks which are used to deform the generic 3D face model. Experimental results on the Color FERET
and CMU multi-PIE databases confirm our framework is effective in creating realistic 3D face models that can be used in
many computer vision applications, such as 3D face recognition at a distance.
ThBT6 Dolmabahçe Hall A
Text Analysis and Detection Regular Session
Session chair: Kholmatov, Alisher (TUBITAK UEKAE)
13:30-13:50, Paper ThBT6.1
Text Detection using Edge Gradient and Graph Spectrum
Zhang, Jing, Univ. of South Florida
Kasturi, Rangachar, Univ. of South Florida
In this paper, we propose a new unsupervised text detection approach which is based on Histogram of Oriented Gradient
and Graph Spectrum. By investigating the properties of text edges, the proposed approach first extracts text edges from
an image and localize candidate character blocks using Histogram of Oriented Gradients, then Graph Spectrum is utilized
to capture global relationship among candidate blocks and cluster candidate blocks into groups to generate bounding boxes
of text objects in the image. The proposed method is robust to the color and size of text. ICDAR 2003 text locating dataset
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