Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper TuAT9.24
Attacking Iris Recognition: An Efficient Hill-Climbing Technique
Rathgeb, Christian, Univ. of Salzburg
Uhl, Andreas, Univ. of Salzburg
In this paper we propose a modified hill-climbing attack to iris biometric systems. Applying our technique we are able to
effectively gain access to iris biometric systems at very low effort. Furthermore, we demonstrate that reconstructing approximations
of original iris images is highly non-trivial.
09:00-11:10, Paper TuAT9.25
Face Recognition at-a-Distance using Texture, Dense- and Sparse-Stereo Reconstruction
Rara, Ham, CVIP Lab. Univ. of Louisville
Ali, Asem, Univ. of Louisville
Elhabian, Shireen, Univ. of Louisville
Starr, Thomas, Univ. of Louisville
Farag, Aly A., Univ. of Louisville
This paper introduces a framework for long-distance face recognition using dense and sparse stereo reconstruction, with
texture of the facial region. Two methods to determine correspondences of the stereo pair are used in this paper: (a) dense
global stereo-matching using maximum-a-posteriori Markov Random Fields (MAP-MRF) algorithms and (b) Active Appearance
Model (AAM) fitting of both images of the stereo pair and using the fitted AAM mesh as the sparse correspondences.
Experiments are performed using combinations of different features extracted from the dense and sparse
reconstructions, as well as facial texture. The cumulative rank curves (CMC), which are generated using the proposed
framework, confirms the feasibility of the proposed work for long distance recognition of human faces.
09:00-11:10, Paper TuAT9.26
Automatic Asymmetric 3D-2D Face Recognition
Huang, Di, Ec. Centrale de Lyon
Ardabilian, Mohsen, Ec. Centrale de Lyon
Wang, Yunhong, Beihang Univ.
Chen, Liming, Ec. Centrale de Lyon
3D Face recognition has been considered as a major solution to deal with unsolved issues of reliable 2D face recognition
in recent years, i.e. lighting and pose variations. However, 3D techniques are currently limited by their high registration
and computation cost. In this paper, an asymmetric 3D-2D face recognition method is presented, enrolling in textured 3D
whilst performing automatic identification using only 2D facial images. The goal is to limit the use of 3D data to where it
really helps to improve face recognition accuracy. The proposed approach contains two separate matching steps: Sparse
Representation Classifier (SRC) is applied to 2D-2D matching, while Canonical Correlation Analysis (CCA) is exploited
to learn the mapping between range LBP faces (3D) and texture LBP faces (2D). Both matching scores are combined for
the final decision. Moreover, we propose a new preprocessing pipeline to enhance robustness to lighting and pose effects.
The proposed method achieves better experimental results in the FRGC v2.0 dataset than 2D methods do, but avoiding
the cost and inconvenience of data acquisition and computation of 3D approaches.
09:00-11:10, Paper TuAT9.27
Model and Score Adaptation for Biometric Systems: Coping with Device Interoperability and Changing Acquisition
Conditions
Poh, Norman, Univ. of Surrey
Kittler, Josef, Univ. of Surrey
Marcel, Sebastien, IDIAP Res. Inst. EPFL
Matrouf, Driss, Univ. d’Avignon et des Pays de Vaucluse
Bonastre, Jean-Francois, Univ. d’Avignon et des Pays de Vaucluse
The performance of biometric systems can be significantly affected by changes in signal quality. In this paper, two types
of changes are considered: change in acquisition environment and in sensing devices. We investigated three solutions: (I)
model-level adaptation, (ii) score-level adaptation (normalisation), and (iii) the combination of the two, called compound
adaptation. In order to cope with the above changing conditions, the model-level adaptation attempts to update the param-
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