Abstract book (pdf) - ICPR 2010

in a watch list or checking for duplicates in a citizen ID card system, there are many technical challenges in building a solution
because the size of the database can be very large (often in 100s of millions) and the intrinsic errors with the underlying
biometrics engines. Often multi-modal biometrics is proposed as a way to improve the underlying biometrics accuracy
performance. In this paper, we propose a score based fusion scheme tailored for identification applications. The proposed
algorithm uses a gradient descent method to learn weights for each modality such that weighted sum of genuine scores is
larger than the weighted sum of all the impostor scores. During the identification phase, top K candidates from each modality
are retrieved and a super-set of identities is constructed. Using the learnt weights, we compute the weighted score for
all the candidates in the superset. The highest scoring candidate is declared as the top candidate for identification. The
proposed algorithm has been tested using NIST BSSR-1 dataset and results in terms of accuracy as well as the speed (execution
time) are shown to be far superior than the published results on this dataset.
09:00-11:10, Paper TuAT9.51
Robust ECG Biometrics by Fusing Temporal and Cepstral Information
Li, Ming, Univ. of Southern California
Narayanan, Shrikanth, Univ. of Southern California
The use of vital signs as a biometric is a potentially viable approach in a variety of application scenarios such as security
and personalized health care. In this paper, a novel robust Electrocardiogram (ECG) biometric algorithm based on both
temporal and cepstral information is proposed. First, in the time domain, after pre-processing and normalization, each
heartbeat of the ECG signal is modeled by Hermite polynomial expansion (HPE) and support vector machine (SVM).
Second, in the homomorphic domain, cepstral features are extracted from the ECG signals and modeled by Gaussian mixture
modeling (GMM). In the GMM framework, heteroscedastic linear discriminant analysis and GMM super vector kernel
is used to perform feature dimension reduction and discriminative modeling, respectively. Finally, fusion of both temporal
and cepstral system outcomes at the score level is used to improve the overall performance. Experiment results show that
the proposed hybrid approach achieves 98.3% accuracy and 0.5% equal error rate on the MIT-BIH Normal Sinus Rhythm
Database.
09:00-11:10, Paper TuAT9.52
A Comparative Study of Facial Landmark Localization Methods for Face Recognition using HOG Descriptors
Monzo, David, Univ. Pol. Valencia
Albiol, Alberto, Univ. Pol. Valencia
Albiol, Antonio, Univ. Pol. Valencia
Mossi, Jose M., Univ. Pol. Valencia
This paper compares several approaches to extract facial landmarks and studies their influence on face recognition problems.
In order to obtain fair comparisons, we use the same number of facial landmarks and the same type of descriptors
(HOG descriptors) for each approach. The comparative results are obtained using FERET and FRGC datasets and show
that better recognition rates are obtained when landmarks are located at real facial fiducial points. However, if the automatic
detection of these is compromised by the difficulty of the images, better results are obtained using fixed landmarks grids.
09:00-11:10, Paper TuAT9.53
Confidence Weighted Subspace Projection Techniques for Robust Face Recognition in the Presence of Partial Occlusio
Struc, Vitomir, Univ. of Ljubljana
Dobrišek, Simon, Univ. of Ljubljana
Pavesic, Nikola, Univ. of Ljubljana
Subspace projection techniques are known to be susceptible to the presence of partial occlusions in the image data. To
overcome this susceptibility, we present in this paper a confidence weighting scheme that assigns weights to pixels according
to a measure, which quantifies the confidence that the pixel in question represents an outlier. With this procedure
the impact of the occluded pixels on the subspace representation is reduced and robustness to partial occlusions is obtained.
Next, the confidence weighting concept is improved by a local procedure for the estimation of the subspace representation.
Both the global weighting approach and the local estimation procedure are assessed in face recognition experiments on
the AR database, where encouraging results are obtained with partially occluded facial images.
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