193123
Incorporating Image Quality in Multi-algorithm Fingerprint Verification
The effect of image quality on the performance of fingerprint verification is studied. In particular, we investigate the performance of two fingerprint matchers based on minutiae and ridge information as well as their score-level combination under varying fingerprint image quality. The ridge-based system is found to be more robust to image quality degradation than the minutiae-based system. We exploit this fact by introducing an adaptive score fusion scheme based on automatic quality estimation in the spatial frequency domain. The proposed scheme leads to enhanced performance over a wide range of fingerprint image quality.
7693083
Pores and Ridges: Fingerprint Matching Using Level 3 Features
Fingerprint friction ridge details are generally described in a hierarchical order at three levels, namely, Level 1 (pattern), Level 2 (minutiae points) and Level 3 (pores and ridge shape). Although high resolution sensors (~1000 dpi) have become commercially available and have made it possible to reliably extract Level 3 features, most automated fingerprint identification systems (AFIS) employ only Level 1 and Level 2 features. As a result, increasing the scan resolution does not provide any matching performance improvement (1998). We develop a matcher that utilizes Level 3 features, including pores and ridge contours, for 1000 dpi fingerprint matching. Level 3 features are automatically extracted using wavelet transform and Gabor filters and are locally matched using the ICP algorithm. Our experiments on a median-sized database show that Level 3 features carry significant discriminatory information. EER values are reduced (relatively ~20%) when Level 3 features are employed in combination with Level 1 and 2 features
672758
Fingerprint Quality Indices for Predicting Authentication Performance
The performance of an automatic fingerprint authentication system relies heavily on the quality of the captured fingerprint images. In this paper, two new quality indices for fingerprint images are developed. The first index measures the energy concentration in the frequency domain as a global feature. The second index measures the spatial coherence in local regions. We present a novel framework for evaluating and comparing quality indices in terms of their capability of predicting the system performance at three different stages, namely, image enhancement, feature extraction and matching. Experimental results on the IBM-HURSLEY and FVC2002 DB3 databases demonstrate that the global index is better than the local index in the enhancement stage (correlation of 0.70 vs. 0.50) and comparative in the feature extraction stage (correlation of 0.70 vs. 0.71). Both quality indices are effective in predicting the matching performance, and by applying a quality-based weighting scheme in the matching algorithm, the overall matching performance can be improved; a decrease of 1.94% in EER is observed on the FVC2002 DB3 database.
6783224
Fingerprint Deformation Models Using Minutiae Locations and Orientations
Nonlinear deformations in fingerprint images, arising from the elasticity of the skin as well as the pressure and movement of the finger during image acquisition, lead to difficulties in establishing a match between multiple impressions acquired from the same finger. One solution to this problem is to estimate and remove the relative deformations prior to the matching stage. In this paper, these relative deformations are represented as an average deformation model based on minutiae locations and orientations using 2-D thin plate splines (TPS). The estimated average deformation is used to pre-distort a template prior to matching it with a query image in a verification task. Experimental results show that the use of minutiae locations and orientations to estimate the deformation leads to a more representative deformation model than using minutiae locations only. An index of deformation based on the bending energy is also proposed to select templates with the least variability in the deformations. The EER goes down by ap 1.1% when we incorporate minutiae orientation information and use the template selection strategy.
8410980
Quality-based Score Level Fusion in Multibiometric Systems
The quality of biometric samples has a significant impact on the accuracy of a matcher. Poor quality biometric samples often lead to incorrect matching results because the features extracted from these samples are not reliable. Therefore, dynamically assigning weights to the outputs of individual matchers based on the quality of the samples presented at the input of the matchers can improve the overall recognition performance of a multibiometric system. We propose a likelihood ratio-based fusion scheme that takes into account the quality of the biometric samples while combining the match scores provided by the matchers. Instead of estimating the quality of the template and query images individually, we estimate a single quality metric for each template-query pair based on the local image quality measures. Experiments on a database of 320 users with iris and fingerprint modalities demonstrate the advantages of utilizing the quality information in multibiometric systems
10299300
On the development of a fully automated cryosection image registration system
The CCD cryosection images for the male collected in the National Library of Medicine's Visible Human Project were registered through a procedure involving significant human intervention. In addition, several large misalignments were visible in the registered CCD data for the male. To expedite the exploitation of the visible human cryosection data sets, there is a need to develop a highly accurate yet efficient image registration tool. In this paper, we present the current status of the development of such a fully automated registration system. This system is based on the maximization of mutual information criterion. One key issue of this approach is a robust optimization scheme. The method developed in this paper is to estimate the initial search point through segmentations of regions of interest for each cryosection image slice. Experimental results using the 70 mm cryosection image data for the male are presented in this paper.
8090728
The SAHARA Model for Service Composition across Multiple Providers
Services are capabilities that enable applications and are of crucial importance to pervasive computing in next-generation networks. Service Composition is the construction of complex services from primitive ones; thus enabling rapid and flexible creation of new services. The presence of multiple independent service providers poses new and significant challenges. Managing trust across providers and verifying the performance of the components in composition become essential issues. Adapting the composed service to network and user dynamics by choosing service providers and instances is yet another challenge. In SAHARA, we are developing a comprehensive architecture for the creation, placement, and management of services for composition across independent providers. In this paper, we present a layered reference model for composition based on a classification of different kinds of composition. We then discuss the different overarching mechanisms necessary for the successful deployment of such an architecture through a variety of case-studies involving composition.
39601946
An Evolutionary System and Its Application to Automatic Image Segmentation
In this paper, an algorithm built on the notion of evolutionary system is proposed. During the evolution of distributed tribes, the individuals making up the tribes cooperatively effect pixel communication from one agent to the other in order to improve the homogeneity of the ensemble of the image regions that they represent. The proposed Artificial Coevolving Tribes, containing evolution, mature, propagate, and die, is explored in the context of image. Meanwhile, the given image is recognized and segmented. Stability and scale control of the algorithm is maintained. Results obtained on natural scenes are presented with the evolutionary process.
1446328
Solving Large-Scale Nonlinear Programming Problems by Constraint Partitioning
In this paper, we present a constraint-partitioning approach for finding local optimal solutions of large-scale mixed-integer nonlinear programming problems (MINLPs). Based on our observation that MINLPs in many engineering applications have highly structured constraints, we propose to partition these MINLPs by their constraints into subproblems, solve each subproblem by an existing solver, and resolve those violated global constraints across the subproblems using our theory of extended saddle points. Constraint partitioning allows many MINLPs that cannot be solved by existing solvers to be solvable because it leads to easier subproblems that are significant relaxations of the original problem. The success of our approach relies on our ability to resolve violated global constraints efficiently, without requiring exhaustive enumerations of variable values in these constraints. We have developed an algorithm for automatically partitioning a large MINLP in order to minimize the number of global constraints, an iterative method for determining the optimal number of partitions in order to minimize the search time, and an efficient strategy for resolving violated global constraints. Our experimental results demonstrate significant improvements over the best existing solvers in terms of solution time and quality in solving a collection of mixed-integer and continuous nonlinear constrained optimization benchmarks. 
 
Research supported by National Science Foundation Grant IIS 03-12084.
10353520
Finding Self-Similarities in Opportunistic People Networks
Opportunistic network is a type of delay tolerant networks (DTN) where network communication opportunities appear opportunistic. In this study, we investigate opportunistic network scenarios based on public network traces, and our contributions are the following: First, we identify the censorship issue in network traces that usually leads to strongly skewed distribution of the measurements. Based on this knowledge, we then apply the Kaplan-Meier Estimator to calculate the survivorship of network measurements, which is used in designing our proposed censorship removal algorithm (CRA) that is used to recover censored data. Second, we perform a rich set of analysis illustrating that UCSD and Dartmouth network traces show strong self-similarity, and can be modeled as such. Third, we pointed out the importance of these newly revealed characteristics in future development and evaluation of opportunistic networks.