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

Detection of Singular Points from Fingerprint Images Using an Innovative Algorithm

Fingerprint scrutiny is typically based on the location and pattern of detected singular points in the images. These singular points (cores and deltas) not only represent the characteristics of local ridge patterns but also determine the topological structure (i.e., fingerprint type) and largely influence the orientation field. In this report, there is an innovative algorithm for singular points detection. After an initial detection using the conventional Poincare Index method, a so-called DORIVAC feature is used to remove spurious singular points. Then, the optimal combination of singular points is selected to minimize the difference between the original orientation field and the model-based orientation field reconstructed using the singular points. A core-delta relation is used as a global constraint for the final selection of singular points. Keywords: Orientation field, Poincare´ Index, Singular points, topological structur

A bisector line field approach to interpolation of orientation fields

We propose an approach to the problem of global reconstruction of an orientation field. The method is based on a geometric model called "bisector line fields", which maps a pair of vector fields to an orientation field, effectively generalizing the notion of doubling phase vector fields. Endowed with a well chosen energy minimization problem, we provide a polynomial interpolation of a target orientation field while bypassing the doubling phase step. The procedure is then illustrated with examples from fingerprint analysis

A new method for the detection of singular points in fingerprint images

Automatic biometric identification based on fingerprintsis still one of the most reliable identification method in criminaland forensic applications. A critical step in fingerprintanalysis without human intervention is to automatically andreliably extract singular points from the input fingerprintimages. These singular points (cores and deltas) not onlyrepresent the characteristics of local ridge patterns but alsodetermine the topological structure (i.e., fingerprint type)and largely influence the orientation field. Poincaré Indexbasedmethods are one of the most common for singularpoints detection. However, these methods usually result inmany spurious detections. Therefore, we propose an enhancedversion of the method presented by Zhou et al. [13]that introduced a feature called DORIC to improve the detection.Our principal contribution lies in the adoption of asmoothed orientation field and in the formulation of a newalgorithm to analyze the DORIC feature. Experimental resultsshow that the proposed algorithm is accurate and robust,giving better results than the best reported results sofar, with improvements in the range of 5% to 7%

LocNet: Global localization in 3D point clouds for mobile vehicles

Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge. In this paper, a solution to this problem is presented by achieving place recognition and metric pose estimation in the global prior map. Specifically, we present a semi-handcrafted representation learning method for LiDAR point clouds using siamese LocNets, which states the place recognition problem to a similarity modeling problem. With the final learned representations by LocNet, a global localization framework with range-only observations is proposed. To demonstrate the performance and effectiveness of our global localization system, KITTI dataset is employed for comparison with other algorithms, and also on our long-time multi-session datasets for evaluation. The result shows that our system can achieve high accuracy.Comment: 6 pages, IV 2018 accepte