Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper TuAT9.17
Probabilistic Measure for Signature Verification based on Bayesian Learning
Pu, Danjun, State Univ. of New York at Buffalo
Srihari, Sargur
Signature verification is a common task in forensic document analysis. The goal is to make a decision whether a questioned
signature belongs to a set of known signatures of an individual or not. In a typical forgery case a very limited number of
known signatures may be available, with as few as four or five knowns \cite{Stev95}. Here we describe a fully Bayesian
approach which overcomes the limitation of having too few genuine samples. The algorithm has three steps: Step 1: Learn
prior distributions of parameters from a population of known signatures; Step 2: Determine the posterior distributions of
parameters using the genuine samples of a particular person; Step 3: Determine probabilities of the query from both genuine
and forgery classes and the Log Likelihood Ratio (LLR) of the query. Rather than give a hard decision, this method provides
a probabilistic measure LLR of the decision and the performance of the Bayesian Learning is improved especially in the
case of limited known samples.
09:00-11:10, Paper TuAT9.18
Gender Classification using on Single Frontal Image Per Person: Combination of Appearance and Geometric based
Features
Mozaffari, Saeed, Semnan Univ.
Behravan, Hamid, Semnan Univ.
Akbari, Rohollah, Qazvin Azad Univ.
Today, many social interactions and services depend on gender. In this paper, we introduce a single image gender classification
algorithm using combination of appearance-based and geometric-based features. These include Discrete Cosine
Transform (DCT), and Local Binary Pattern (LBP), and geometrical distance feature (GDF). The novel feature, GDF proposed
in this paper, is inspired from physiological differences between male and female faces. Combination of appearance-based
features (DCT and LBP) with geometric-based feature (GDF) leads to higher gender classification accuracy.
Our system estimates gender of the input image based on the majority rule. If the results of DCT and LBP features are not
identical, gender classification will be based on GDF feature. The proposed method was evaluated on two databases: AR
and ethnic. Experimental results show that the novel geometric feature improves the gender classification accuracy by
13%.
09:00-11:10, Paper TuAT9.19
Residual Analysis for Fingerprint Orientation Modeling
Jirachaweng, Suksan, Kasetsart Univ.
Hou, Zujun, Inst. For Infocomm Res.
Li, Jun, Inst. For Infocomm Res.
Yau, Wei-Yun, Inst. For Infocomm Res.
Areekul, Vutipong, Kasetsart Univ.
This paper presents a novel method for fingerprint orientation modeling, which executes in two phases. Firstly, the orientation
field is reconstructed through fitting to a lower order Legendre polynomial basis to capture the global orientation
pattern. Then the preliminary model around the singular region is dynamically refined by fitting to a higher order Legendre
polynomial basis. The singular region is automatically detected through the analysis on the orientation residual field between
the original orientation field and the orientation model. The method has been evaluated using the FVC 2004 data
sets and compared with state-of-the-arts. Experiments turn out that the propose method attains higher accuracy in fingerprint
matching and singularity preservation.
09:00-11:10, Paper TuAT9.20
Dynamic Amelioration of Resolution Mismatches for Local Feature based Identity Inference
Wong, Yongkang, NICTA
Sanderson, Conrad, NICTA
Mau, Sandra, NICTA
Lovell, Brian Carrington, The Univ. of Queensland
While existing face recognition systems based on local features are robust to issues such as misalignment, they can exhibit
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