Distorted Fingerprint Verification System

Fingerprint verification is one of the most reliable personal identification methods. Fingerprint matching is affected by non-linear distortion introduced in fingerprint impression during the image acquisition process. This non-linear deformation changes both the position and orientation of minutiae. The proposed system operates in three stages: alignment based fingerprint matching, fuzzy clustering and classifier framework. First, an enhanced input fingerprint image has been aligned with the template fingerprint image and matching score is computed. To improve the performance of the system, a fuzzy clustering based on distance and density has been used to cluster the feature set obtained from the fingerprint matcher. Finally a classifier framework has been developed and found that cost sensitive classifier produces better results. The system has been evaluated on fingerprint database and the experimental result shows that system produces a verification rate of 96%. This system plays an important role in forensic and civilian applications.Biometric, Fingerprints, Distortion, Fuzzy Clustering, Cost Sensitive Classifier

Occurrence and associative value of non-identifiable fingermarks

Fingermarks that have insufficient characteristics for identification often have discernible characteristics that could form the basis for lesser degrees of correspondence or probability of occurrence within a population. Currently, those latent prints that experts judge to be insufficient for identification are not used as associative evidence. How often do such prints occur and what is their potential value for association? The answers are important. We could be routinely setting aside a very important source of associative evidence, with high potential impact, in many cases; or such prints might be of very low utility, adding very little, or only very rarely contributing to cases in a meaningful way. The first step is to better understand the occurrence and range of associative value of these fingermarks. The project goal was to explore and test a theory that in large numbers of cases fingermarks of no value for identification purposes occur and are readily available, though not used, and yet have associative value that could provide useful information. Latent fingermarks were collected from nine state and local jurisdictions. Fingermarks included were those (1) collected in the course of investigations using existing jurisdictional procedures, (2) originally assessed by the laboratory as of no value for identification (NVID), (3) re-assessed by expert review as NVID, but with least three clear and reliable minutiae in relationship to one another, and (4) determined to show at least three auto-encoded minutiae. An expected associative value (ESLR) for each mark was measured, without reference to a putative source, based on modeling within-variability and between-variability of AFIS scores. This method incorporated (1) latest generation feature extraction, (2) a (minutiae-only) matcher, (3) a validated distortion model, and (4) NIST SD27 database calibration. Observed associative value distributions were determined for violent crimes, property crimes, and for existing objective measurements of latent print quality. 750 Non Identifiable Fingermarks (NIFMs) showed values of Log10 ESLR ranging from 1.05 to 10.88, with a mean value of 5.56 (s.d. 2.29), corresponding to an ESLR of approximately 380,000. It is clear that there are large numbers of cases where NIFMs occur that have high potential associative value as indicated by the ESLR. These NIFMs are readily available, but not used, yet have associative value that could provide useful information. These findings lead to the follow-on questions, “How useful would NIFM evidence be in actual practice?” and, “What developments or improvements are needed to maximize this contribution?

Distorted Fingerprint Verification System

Fingerprint verification is one of the most reliable personal identification methods. Fingerprint matching is affected by non-linear distortion introduced in fingerprint impression during the image acquisition process. This non-linear deformation changes both the position and orientation of minutiae. The proposed system operates in three stages: alignment based fingerprint matching, fuzzy clustering and classifier framework. First, an enhanced input fingerprint image has been aligned with the template fingerprint image and matching score is computed. To improve the performance of the system, a fuzzy clustering based on distance and density has been used to cluster the feature set obtained from the fingerprint matcher. Finally a classifier framework has been developed and found that cost sensitive classifier produces better results. The system has been evaluated on fingerprint database and the experimental result shows that system produces a verification rate of 96%. This system plays an important role in forensic and civilian applications

Facilitating sensor interoperability and incorporating quality in fingerprint matching systems

This thesis addresses the issues of sensor interoperability and quality in the context of fingerprints and makes a three-fold contribution. The first contribution is a method to facilitate fingerprint sensor interoperability that involves the comparison of fingerprint images originating from multiple sensors. The proposed technique models the relationship between images acquired by two different sensors using a Thin Plate Spline (TPS) function. Such a calibration model is observed to enhance the inter-sensor matching performance on the MSU dataset containing images from optical and capacitive sensors. Experiments indicate that the proposed calibration scheme improves the inter-sensor Genuine Accept Rate (GAR) by 35% to 40% at a False Accept Rate (FAR) of 0.01%. The second contribution is a technique to incorporate the local image quality information in the fingerprint matching process. Experiments on the FVC 2002 and 2004 databases suggest the potential of this scheme to improve the matching performance of a generic fingerprint recognition system. The final contribution of this thesis is a method for classifying fingerprint images into 3 categories: good, dry and smudged. Such a categorization would assist in invoking different image processing or matching schemes based on the nature of the input fingerprint image. A classification rate of 97.45% is obtained on a subset of the FVC 2004 DB1 database

“Implementation on Distorted Fingerprints”

Flexible distortion of fingerprints is the main origin of false non-match. While this origin disturbs all fingerprint recognition applications, it is mainly risk in negative recognition applications, such as watch list duplication applications. In such things, malignant user mayconsciously distort their fingerprints to hide his originality or identification. This paper, suggested novel algorithms to identify and modify skin distortion based on a single fingerprint image. Distortion detection is displayed as a two-class categorization problem, for which the registered ridge orientation map and period map of a fingerprint are beneficial as the feature vector and a SVM classifier is trained to act the classification task. Distortion rectification (or equivalently distortion field estimation) is viewed as a regression complication, where provide the input as a distorted fingerprint and generate the output as distortion field. To clarify this Problem, offline and online stages are important. A database (called reference database) of various distorted reference fingerprints and corresponding distortion fields is built in the offline stage, and then in the online stage, the closest neighbor of the input fingerprint is organized in the reference database and the corresponding distortion field is used to transform (Convert) the input distorted fingerprint into a normal undistorted fingerprints

Nonlinear RGB-to-XYZ Mapping for Device Calibration

We introduce a new non-linear method for RGB-to-XYZ color calibration based on the technique of thin plate splines. Originally, thin plate splines were designed for deformable matching between 2-dimensional images for object recognition. We use 3-dimensional thin plate splines to map between sets of RGB device coordinates and corresponding sets of CIE XYZ coordinates. Tests calibrating several displays as well as a camera show thin plate spline calibration to be more accurate than existing linear or non-linear calibration methods

Quantifying the Limits of Fingerprint Variability

Fingerprints are one of the most widely used identification features in both the biometric and forensic fields. However, the comparison and identification of fingerprints is made difficult by fingerprint variability arising from distortion. This study quantifies the limits of fingerprint variability when subject to heavy distortion, and the variability observed in repeated inked planar impressions. Fingers were video recorded performing several distortion conditions under heavy deposition pressure: left, right, up, and down translation of the finger, clockwise and counter-clockwise torque of the finger, and planar impressions. Fingerprint templates, containing `true\u27 minutiae locations, were then created from 10 inked planar impressions for 30 separate fingers. The 30 fingers studied consisted of 10 right slant loops, 10 plain arches, and 10 plain whorls. A minimal amount of variability, .18 mm globally, was observed for minutiae in inked planar impressions. When subject to heavy distortion minutiae can be displaced by upwards of 3 mm and their orientation altered by as much as 30 degrees. Minutiae displacements of 1 mm and 10 degree changes in orientation are readily observed. The results of this study will allow fingerprint examiners to identify and understand the degree of variability that can be reasonably expected throughout the various regions of fingerprints