14-02-2013, 04:17 PM
Altered Fingerprints: Analysis and Detection
Altered Fingerprint.doc (Size: 92.5 KB / Downloads: 66)
Abstract
The widespread deployment of Automated Fingerprint Identification Systems (AFIS) in law enforcement and border control applications has heightened the need for ensuring that these systems are not compromised. While several issues related to fingerprint system security have been investigated, including the use of fake fingerprints for masquerading identity, the problem of fingerprint alteration or obfuscation has received very little attention. Fingerprint obfuscation refers to the deliberate alteration of the fingerprint pattern by an individual for the purpose of masking his identity. Several cases of fingerprint obfuscation have been reported in the press. Fingerprint image quality assessment software (e.g., NFIQ) cannot always detect altered fingerprints since the implicit image quality due to alteration may not change significantly.
The main contributions of this paper are:
1) Compiling case studies of incidents where individuals were found to have altered their fingerprints for circumventing AFIS,
2) Investigating the impact of fingerprint alteration on the accuracy of a commercial fingerprint matcher,
3) Classifying the alterations into three major categories and suggesting possible countermeasures,
4) Developing a technique to automatically detect altered fingerprints based on analyzing orientation field and minutiae distribution, and
5) Evaluating the proposed technique and the NFIQ algorithm on a large database of altered fingerprints provided by a law enforcement agency. Experimental results show the feasibility of the proposed approach in detecting altered fingerprints and highlight the need to further pursue this problem.
2. ANALYSIS OF MINUTIAE DISTRIBUTION
In this module, a minutia in the fingerprint indicates ridge characteristics such as ridge ending or ridge bifurcation. Almost all fingerprint recognition systems use minutiae for matching. In addition to the abnormality observed in orientation field, we also noted that minutiae distribution of altered fingerprints often differs from that of natural fingerprints.
Based on the minutiae extracted from a fingerprint by the open source minutiae extractor in NBIS, a minutiae density map is constructed by using the Parzen window method with uniform kernel function.
SYSTEM REQUIREMENT SPECIFICATION
HARDWARE REQUIREMENTS
• System : Pentium IV 2.4 GHz.
• Hard Disk : 80 GB.
• Monitor : 15 VGA Color.
• Mouse : Logitech.
• Ram : 512 MB.
SOFTWARE REQUIREMENTS
• Operating system : Windows 7 Ultimate
• Front End : Visual Studio 2010
• Coding Language : C#.NET
• Database : SQL Server 2008
DETECTION OF ALTERED FINGERPRINTS
A. NORMALIZATION
An input fingerprint image is normalized by cropping a rectangular region of the fingerprint, which is located at the center of the fingerprint and aligned along the longitudinal direction of the finger, using the NIST Biometric Image Software (NBIS). This step ensures that the features extracted in the subsequent steps are invariant with respect to translation and rotation of finger.
B. ORIENTATION FIELD ESTIMATION
The orientation field of the fingerprint is computed using the gradient-based method. The initial orientation field is smoothed averaging filter, followed by averaging the orientations in pixel blocks. A foreground mask is obtained by measuring the dynamic range of gray values of the fingerprint image in local blocks and morphological process for filling holes and removing isolated blocks is performed.