Abstract book (pdf) - ICPR 2010

In this article, a new fragile, blind, high payload capacity, ROI (Region of Interest) preserving Medical image watermarking
(MIW) technique in the spatial domain for gray scale medical images is proposed. We present a watermarking scheme
that combines lossless data compression and encryption technique in application to medical images. The effectiveness of
the proposed scheme, proven through experiments on various medical images through various image quality measure matrices
such as PSNR, MSE and MSSIM enables us to argue that, the method will help to maintain Electronic Patient Report(EPR)/DICOM
data privacy and medical image integrity.
TuBT6 Topkapı Hall B
Face Recognition – I Regular Session
Session chair: Ross, Arun (West Virginia Univ.)
13:0-13:50, Paper TuBT6.1
Efficient Facial Attribute Recognition with a Spatial Codebook
Ijiri, Yoshihisa, OMRON Corp.
Lao, Shihong, OMRON Corp.
Han, Tony X., Univ. of Missouri
Murase, Hiroshi, Nagoya Univ.
There is a large number of possible facial attributes such as hairstyle, with/without glasses, with/without mustache, etc.
Considering large number of facial attributes and their combinations, it is difficult to build attributes classifiers for all possible
combinations needed in various applications, especially at the designing stage. To tackle this important and challenging
problem, we propose a novel efficient facial attributes recognition algorithm using a learned spatial codebook.
The Maximum Entropy and Maximum Orthogonality (MEMO) criterion is followed to learn the spatial codebook. With
a spatial codebook constructed at the designing stage, attribute classifiers can be trained on demand with a small number
of exemplars with high accuracy on the testing data. Meanwhile, up to 600 times speedup is achieved in the on-demand
training process, compared to current state-of-the-art method. The effectiveness of the proposed method is supported by
convincing experimental results.
13:50-14:10, Paper TuBT6.2
Feature Space Hausdorff Distance for Face Recognition
Chen, Shaokang, NICTA
Lovell, Brian Carrington, The Univ. of Queensland
We propose a novel face image similarity measure based on Hausdorff distance (HD). In contrast to conventional HDbased
measures, which are generally applied in the image space (such as edge maps or gradient images), the proposed
HD-based similarity measure is applied in the feature space. By extending the concept of HD using a variable radius and
reference set, we can generate a neighbourhood set for HD measures in feature space and then apply this concept for classification.
Experiments on the Labeled Faces in the Wild; and FRGC datasets show that the proposed measure improves
the overall classification performance quite dramatically, especially under the highly desirable low false acceptance rate
conditions.
14:10-14:30, Paper TuBT6.3
How to Measure Biometric Information?
Sutcu, Yagiz, Pol. Inst. of New York Univ.
Sencar, Husrev Taha, TOBB Univ. of Ec. and Tech.
Memon, Nasir, Pol. Inst. of New York Univ.
Being able to measure the actual information content of biometrics is very important but also a challenging problem. Main
difficulty here is not only related to the selected feature representation of the biometric data, but also related to the matching
algorithm employed in biometric systems. In this paper, we propose a new measure for measuring biometric information
using relative entropy between intra-user and inter-user distance distributions. As an example, we evaluated the proposed
measure on a face image dataset.
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