Abstract book (pdf) - ICPR 2010

The advance of falsification technology increases security concerns and gives biometrics an important role in security solutions.
The electrocardiogram (ECG) is an emerging biometric that does not need liveliness verification. There is strong
evidence that ECG signals contain sufficient discriminative information to allow the identification of individuals from a
large population. Most approaches rely on ECG data and the fiducia of different parts of the heartbeat waveform. However
non-fiducial approaches have proved recently to be also effective, and have the advantage of not relying critically on the
accurate extraction of fiducia data. In this paper, we propose a new % NEW DAV non-fiducial ECG biometric identification
method based on data compression techniques, namely the Ziv-Merhav cross parsing algorithm for symbol sequences
(strings). Our method relies on a string similarity measure derived from algorithmic cross complexity concept and its compression-based
approximation. NEW DAV We present results on real data, one-lead ECG, acquired during a concentration
task, from 19 healthy individuals. Our approach achieves 100% subject recognition rate despite the existence of differentiated
stress states.
09:00-11:10, Paper ThAT9.54
Multimodal Human Computer Interaction with MIDAS Intelligent Infokiosk
Karpov, Alexey, Russian Acad. of Sciences
Ronzhin, Andrey, Russian Acad. of Sciences
Kipyatkova, Irina, Russian Acad. of Sciences
Ronzhin, Alexander, Russian Acad. of Sciences
Akarun, Lale, Bogazici Univ.
In this paper, we present an intelligent information kiosk called MIDAS (Multimodal Interactive-Dialogue Automaton for
Self-service), including its hardware and software architecture, stages of deployment of speech recognition and synthesis
technologies. MIDAS uses the methodology Wizard of Oz (WOZ) that allows an expert to correct speech recognition
results and control the dialogue flow. User statistics of the multimodal human computer interaction (HCI) have been analyzed
for the operation of the kiosk in the automatic and automated modes. The infokiosk offers information about the
structure and staff of laboratories, the location and phones of departments and employees of the institution. The multimodal
user interface is provided with a touch screen, natural speech input and head and manual gestures, both for ordinary and
physically handicapped users.
09:00-11:10, Paper ThAT9.55
View Invariant Body Pose Estimation based on Biased Manifold Learning
Hur, Dongcheol, Korea Univ.
Lee, Seong-Whan, Korea Univ.
Wallraven, Christian, MPI for Biological Cybernetics
In human body pose estimation, manifold learning is a popular technique for reducing the dimension of 2D images and
3D body configuration data. This technique, however, is especially vulnerable to silhouette variation such as caused by
viewpoint changes. In this paper, we propose a novel approach that combines three separate manifolds for representing
variations in viewpoint, pose and 3D body configuration. We use biased manifold learning to learn these manifolds with
appropriately weighted distances. A set of four mapping functions are then learned by a generalized regression neural network
for added robustness. Despite using only three manifolds, we show that this method can reliably estimate 3D body
poses from 2D images with all learned viewpoints.
09:00-11:10, Paper ThAT9.56
Visual Gaze Estimation by Joint Head and Eye Information
Valenti, Roberto, Univ. of Amsterdam
Lablack, Adel, UMR USTL/CNRS 8022
Sebe, Nicu, Univ. of Trento
Djeraba, Chabane, UMR USTL/CNRS 8022
Gevers, Theo, Univ. of Amsterdam
In this paper, we present an unconstrained visual gaze estimation system. The proposed method extracts the visual field
of view of a person looking at a target scene in order to estimate the approximate location of interest (visual gaze). The
novelty of the system is the joint use of head pose and eye location information to fine tune the visual gaze estimated by
the head pose only, so that the system can be used in multiple scenarios. The improvements obtained by the proposed approach
are validated using the Boston University head pose dataset, on which the standard deviation of the joint visual
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