Abstract book (pdf) - ICPR 2010

Vandermeulen, Dirk
Suetens, Paul, K.U.Leuven
The recognition of faces under varying expressions is one of the current challenges in the face recognition community. In
this paper, we propose a method fusing different complementary approaches each dealing with expression variations. The
first approach uses an isometric deformation model and is based on the largest singular values of the geodesic distance
matrix as an expression-invariant shape descriptor. The second approach performs recognition on the more rigid parts of
the face that are less affected by expression variations. Several fusion techniques are examined for combining the approaches.
The presented method is validated on a subset of 900 faces of the BU-3DFE face database resulting in an equal
error rate of 5.85% for the verification scenario and a rank 1 recognition rate of 94.48% for the identification scenario
using the sum rule as fusion technique. This result outperforms other 3D expression-invariant face recognition methods
on the same database.
09:00-11:10, Paper TuAT9.14
Towards a Best Linear Combination for Multimodal Biometric Fusion
Chia, Chaw, Chia, Chaw, Nottingham Trent Univ.
Sherkat, Nasser, Nottingham Trent Univ.
Nolle, Lars, Nottingham Trent Univ.
Owing to effectiveness and ease of implementation Sum rule has been widely applied in the biometric research field. Different
matcher information has been used as weighting parameters in the weighted Sum rule. In this work, a new parameter
has been devised in reducing the genuine/imposter distribution overlap. It is shown that the overlap region width has the
best generalization performance as the weighting parameter amongst other commonly used matcher information. Furthermore,
it is illustrated that the equal weighted Sum rule can generally perform better than the Equal Error Rate and d-prime
weighted Sum rule. The publicly available databases: the NIST-BSSR1 multimodal biometric and Xm2vts score sets have
been used.
09:00-11:10, Paper TuAT9.15
Slap Fingerprint Segmentation for Live-Scan Devices and Ten-Print Cards
Zhang, Yongliang, Zhejiang Univ. of Technology
Xiao, Gang, Zhejiang Univ. of Technology
Li, Yanmiao, Jiaotong Univ. Dalian
Wu, Hongtao, Hebei Univ. of Tech.
Huang, Yaping, Zhejiang Univ. of Technology
Presented here is a highly accurate and computationally efficient algorithm suitable for slap fingerprint segmentation. The
main advantages of this algorithm are as follows: 1)three-order cumulant is used to roughly segment the foreground; 2)frequency
domain analysis is carried out in local areas to do binarization and fine segmentation; 3)cumulative sum analysis
is applied to extract the knuckle lines; 4)two shape features of the ellipse are adapted to calculate the confidence of each
fingertip candidate. Experimental results show that the algorithm has the characteristic of more robustness against noise
and superior precision, not only for live-scan four finger slaps but also for ten-print-card five finger slaps.
09:00-11:10, Paper TuAT9.16
A Metric of Information Gained through Biometric Systems
Takahashi, Kenta, Hitachi Ltd.
Murakami, Takao, Hitachi Ltd.
We propose a metric of information gained through biometric matching systems. Firstly, we discuss how the information
about the identity of a person is derived from biometric samples through a biometric system, and define the “biometric
system entropy” or BSE. Then we prove that the BSE can be approximated asymptotically by the Kullback-Leibler divergence
D(f_G(x) || f_I(x)) where f_G(x), f_I(x) are PDFs of matching scores between samples from an individuals and
among population. We also discuss how to evaluate D(f_G || f_I) of a biometric system and show a numerical example of
face and fingerprint matching systems.
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