Abstract book (pdf) - ICPR 2010

16:30-16:50, Paper MoBT4.4
Entropy of Feature Point-Based Retina Templates
Jeffers, Jason, RMIT Univ.
Arakala, Arathi, RMIT Univ.
Horadam, Kathy, RMIT Univ.
This paper studies the amount of distinctive information contained in a privacy protecting and compact template of a
retinal image created from the locations of crossings and bifurcations in the choroidal vasculature, otherwise called feature
points. Using a training set of 20 different retina, we build a template generator that simulates one million imposter comparisons
and computes the number of imposter retina comparisons that successfully matched at various thresholds. The
template entropy thus computed was used to validate a theoretical model of imposter comparisons. The simulator and the
model both estimate that 20 bits of entropy can be achieved by the feature point-based template. Our results reveal the
distinctiveness of feature point-based retinal templates, hence establishing their potential as a biometric identifier for high
security and memory intensive applications.
16:50-17:10, Paper MoBT4.5
Hierarchical Fusion of Face and Iris for Personal Identification
Zhang, Xiaobo, Chinese Acad. of Sciences
Sun, Zhenan, Chinese Acad. of Sciences
Tan, Tieniu, Chinese Acad. of Sciences
Most existing face and iris fusion schemes are concerned about improving performance on good quality images under
controlled environments. In this paper, we propose a hierarchical fusion scheme for low quality images under uncontrolled
situations. In the training stage, canonical correlation analysis (CCA) is adopted to construct a statistical mapping from
face to iris in pixel level. In the testing stage, firstly the probe face image is used to obtain a subset of candidate gallery
samples via regression between the probe face and gallery irises, then ordinal representation and sparse representation are
performed on these candidate samples for iris recognition and face recognition respectively. Finally, score level fusion via
min-max normalization is performed to make final decision. Experimental results on our low quality database show the
outperforming performance of proposed method.
MoBT5 Anadolu Auditorium
Image Analysis – II Regular Session
Session chair: Mirmehdi, Majid (Univ. of Bristol)
15:30-15:50, Paper MoBT5.1
Wavelet-Based Texture Retrieval using a Mixture of Generalized Gaussian Distributions
Allili, Mohand Said, Univ. du Québec en Outaouais
In this paper, we address the texture retrieval problem using wavelet distribution. We propose a new statistical scheme to
represent the marginal distribution of the wavelet coefficients using a mixture of generalized Gaussian distributions
(MoGG). The MoGG allows to capture a wide range of histogram shapes, which provides a better description of texture
and enhances texture discrimination. We propose a similarity measurement based on Kullback-Leibler distance (KLD),
which is calculated using MCMC Metropolis-Hastings sampling algorithm. We show that our approach yields better texture
retrieval results than previous methods using only a single probability density function (pdf) for wavelet representation,
or texture energy distribution.
15:50-16:10, Paper MoBT5.2
Adaptive Color Curve Models for Image Matting
Cho, Sunyoung, Yonsei Univ.
Byun, Hyeran, Yonsei Univ.
Image matting is the process of extracting a foreground element from a single image with limited user input. To solve the
inherently ill-posed problem, there exist various methods which use specific color model. One representative method assumes
that the colors of the foreground and background elements satisfy the linear color model. The other recent method
considers line-point color model and point-point color model. In this paper we present a new adaptive color curve model
for image matting. We assume that the colors of local region form curve. Based on these pixels in the local region, we
adaptively construct a curve model using quadratic Bezier curve model. This curve model enables us to derive a matting
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