Abstract book (pdf) - ICPR 2010

eters of the expert systems (classifiers). This approach requires the authenticity of the candidate samples used for adaptation
be known (corresponding to supervised adaptation), or can be estimated (unsupervised adaptation). In comparison, the
score-level adaptation merely involves post processing the expert output, with the objective of rendering the associated
decision threshold to be dependent only on the class priors despite the changing acquisition conditions. Since the above
adaptation strategies treat the underlying biometric experts/classifiers as a black-box, they can be applied to any unimodal
or multimodal biometric system, thus facilitating system-level integration and performance optimisation. Our contributions
are: (I) proposal of compound adaptation; (ii) investigation and comparison of two different quality-dependent score normalisation
strategies; and, (iii) empirical comparison of the merit of the above three solutions on the BANCA face (video)
and speech database.
09:00-11:10, Paper TuAT9.28
Online Boosting OC for Face Recognition in Continuous Video Stream
Huo, Hongwen, Peking Univ.
Feng, Jufu, Peking Univ.
In this paper, we present a novel online face recognition approach for video stream called online boosting OC (output
code). Recently, boosting was successfully used in many study fields such as object detection and tracking. It is one kind
of large margin classifiers for binary classification problems and also efficient for on-line learning. However, face recognition
is a typical multi-class problem. Hence, it is difficult to use boosting in face recognition, especially in an online
version. In our work, we combine online boosting and OC algorithm to solve real-time online multi-class classification
problems. We perform online boosting OC on real-world experiments: face recognition in continuous video stream, and
the results show that our algorithm is accurate and robust.
09:00-11:10, Paper TuAT9.29
On the Dimensionality Reduction for Sparse Representation based Face Recognition
Zhang, Lei, The Hong Kong Pol. Univ.
Yang, Meng, The Hong Kong Pol. Univ.
Feng, Zhizhao, The Hong Kong Pol. Univ.
Zhang, David, The Hong Kong Pol. Univ.
Face recognition (FR) is an active yet challenging topic in computer vision applications. As a powerful tool to represent
high dimensional data, recently sparse representation based classification (SRC) has been successfully used for FR. This
paper discusses the dimensionality reduction (DR) of face images under the framework of SRC. Although one important
merit of SRC is that it is insensitive to DR or feature extraction, a well trained projection matrix can lead to higher FR rate
at a lower dimensionality. An SRC oriented unsupervised DR algorithm is proposed in this paper and the experimental results
on benchmark face databases demonstrated the improvements brought by the proposed DR algorithm over PCA or
random projection based DR under the SRC framework.
09:00-11:10, Paper TuAT9.30
Improved Fingerprint Image Segmentation and Reconstruction of Low Quality Areas
Mieloch, Krzysztof, Univ. of Goettingen
Munk, Axel, Univ. of Goettingen
Mihailescu, Preda, Univ. of Goettingen
One of the main reason for false recognition is noise added to fingerprint images during the acquisition step. Hence, the
improvement of the enhancement step affects general accuracy of automatic recognition systems. In one of our previous
publications we introduced hierarchically linked extended features – the new set of features which not only includes additional
fingerprint features individually but also contains the information about their relationships such as line adjacency
information at minutiae points or links between neighbouring fingerprint lines. In this work we present the application of
the extended features to preprocessing and enhancement. We use structural information for improving the segmentation
step, as well as connecting disrupted fingerprint lines and recovering missing minutiae. Experiments show a decrease in
the error rate in matching.
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