Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper ThAT9.35
Off-Line Signature Verification using Graphical Model
Lv, Hairong
Bai, Xinxin, IBM Res. – China
Yin, Wenjun, IBM Res. – China
Dong, Jin, IBM Res. – China
In this paper, we propose a novel probabilistic graphical model to address the off-line signature verification problem. Different
from previous work, our approach introduces the concept of feature roles according to their distribution in genuine
and forgery signatures, with all these features represented by a unique graphical model. And we propose several new techniques
to improve the performance of the new signature verification system. Results based on 200 persons’ signatures
(16000 signature samples) indicate that the proposed method outperforms other popular techniques for off-line signature
verification with a great improvement.
09:00-11:10, Paper ThAT9.36
Linear Facial Expression Transfer with Active Appearance Models
De La Hunty, Miles, Australian National Univ.
Asthana, Akshay, Australian National Univ.
Goecke, Roland, Univ. of Canberra
The issue of transferring facial expressions from one person’s face to another’s has been an area of interest for the movie
industry and the computer graphics community for quite some time. In recent years, with the proliferation of online image
and video collections and web applications, such as Google Street View, the question of preserving privacy through face
de-identification has gained interest in the computer vision community. In this paper, we focus on the problem of realtime
dynamic facial expression transfer using an Active Appearance Model framework. We provide a theoretical foundation
for a generalisation of two well-known expression transfer methods and demonstrate the improved visual quality of the
proposed linear extrapolation transfer method on examples of face swapping and expression transfer using the AVOZES
data corpus. Realistic talking faces can be generated in real-time at low computational cost.
09:00-11:10, Paper ThAT9.37
Fractal and Multi-Fractal for Arabic Offline Writer Identification
Chaabouni, Aymen, Univ. of Sfax
Boubaker, Houcine, Univ. of Sfax
Kherallah, Monji, Univ. of Sfax
El Abed, Haikal, Technische Universitat Braunschweig
Alimi, Adel M., Univ. of Sfax
In recent years, fractal and multi-fractal analysis have been widely applied in many domains, especially in the field of
image processing. In this direction we present in this paper a novel method for Arabic text-dependent writer identification
based on fractal and multi-fractal features; thus, from the images of Arabic words, we calculate their fractal dimensions
by using the Box-counting method, then we calculate their multi-fractal dimensions by using the method of DLA (Diffusion
Limited Aggregates). To evaluate our method, we used 50 writers of the ADAB database, each writer wrote 288 words
(24 Tunisian cities repeated 12 times) with 2/3 of words are used for the learning phase and the rest is used for the identification.
The results obtained by using knearest neighbor classifier, demonstrate the effectiveness of our proposed method.
09:00-11:10, Paper ThAT9.38
A Simulation Study on the Generative Neural Ensemble Decoding Algorithms
Kim, Sung-Phil, Korea Univ.
Kim, Min-Ki, Korea Univ.
Park, Gwi-Tae, Korea Univ.
Brain-computer interfaces rely on accurate decoding of cortical activity to understand intended action. Algorithms for
neural decoding can be broadly categorized into two groups: direct versus generative methods. Two generative models,
the population vector algorithm (PVA) and the Kalman filter (KF), have been widely used for many intracortical BCI studies,
where KF generally showed superior decoding to PVA. However, little has been known for which conditions each algorithm
works properly and how KF translates the ensemble information. To address these questions, we performed a
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