Abstract book (pdf) - ICPR 2010

TuCT7 Dolmabahçe Hall C
Fingerprint Regular Session
Session chair: Sankur, Bülent (Bogazici Univ.)
15:40-16:00, Paper TuCT7.1
Detecting Altered Fingerprints
Feng, Jianjiang, Tsinghua Univ.
Jain, Anil, Michigan State Univ.
Ross, Arun, West Virginia Univ.
The widespread deployment of Automated Fingerprint Identification Systems (AFIS) in law enforcement and border
control applications has prompted some individuals with criminal background to evade identification by purposely altering
their fingerprints. Available fingerprint quality assessment software cannot detect most of the altered fingerprints since
the implicit image quality does not always degrade due to alteration. In this paper, we classify the alterations observed in
an operational database into three categories and propose an algorithm to detect altered fingerprints. Experiments were
conducted on both real-world altered fingerprints and synthetically generated altered fingerprints. At a false alarm rate of
7%, the proposed algorithm detected 92% of the altered fingerprints, while a well-known fingerprint quality software,
NFIQ, only detected 20% of the altered fingerprints.
16:00-16:20, Paper TuCT7.2
A Variational Formulation for Fingerprint Orientation Modeling
Hou, Zujun, Inst. For Infocomm Res.
Yau, Wei-Yun, Inst. For Infocomm Res.
Fingerprint orientation plays important roles in fingerprint recognition. This paper proposes a framework for modeling
the fingerprint orientation field based on the variational principle. The proposed method does not require any prior information
about the structure of acquired fingerprints. Comparison has been made with respect to state-of-the-arts in fingerprint
orientation modeling.
16:20-16:40, Paper TuCT7.3
Fingerprint Pore Matching based on Sparse Representation
Liu, Feng, The Hong Kong Pol. Univ.
Zhao, Qijun, The Hong Kong Pol. Univ.
Zhang, Lei, The Hong Kong Pol. Univ.
Zhang, David, The Hong Kong Pol. Univ.
This paper proposes an improved direct fingerprint pore matching method. It measures the differences between pores by
using the sparse representation technique. The coarse pore correspondences are then established and weighted based on
the obtained differences. The false correspondences among them are finally removed by using the weighted RANSAC algorithm.
Experimental results have shown that the proposed method can greatly improve the accuracy of existing methods.
16:40-17:00, Paper TuCT7.4
Latent Fingerprint Core Point Prediction based on Gaussian Processes
Su, Chang, Univ. at Buffalo, State Univ. of New York
Srihari, Sargur, Univ. at Buffalo, State Univ. of New York
Core point prediction is of critical importance to latent fingerprints individuality assessment. While tremendous effort
have been made in core point detection, locating core points in latent fingerprints continues to be a difficult problem
because latent prints usually contain only partial images with core points left outside the print. A novel method is proposed
that predicts the locations and orientations of core points for latent fingerprints. The method is based on Gaussian processes
and provides prediction in interpretations of probability rather than binary decision. The accuracy of the method is illustrated
by experiments on a real-life latent fingerprint data set.
17:00-17:20, Paper TuCT7.5
Towards a Better Understanding of the Performance of Latent Fingerprint Recognition in Realistic Forensic Conditions
Puertas, Maria, Univ. Autonoma de Madrid
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