Non Minitia Fingerprint Recognition based on Segmentation

The biometric identification of a person has an advantage over traditional technique. Widely used biometric is Fingerprint to identify and authenticate a person. In this paper we propose Non Minutia Fingerprint Recognition based on Segmentation (NMFRS) algorithm. The variance of each block is determined by segmenting the finger print into 8* 8 blocks. Area of Interest (AOI) is obtained by removing the blocks with minimum variance. Features of Finger Print is obtained by applying Discrete Cosine Transform (DCT) on AOI and converted to major and minor non-overlapping blocks to determine variance. The percentage recognition rate is better in the proposed algorithm compared to the existing algorithms

Computing with functions in the ball

A collection of algorithms in object-oriented MATLAB is described for numerically computing with smooth functions defined on the unit ball in the Chebfun software. Functions are numerically and adaptively resolved to essentially machine precision by using a three-dimensional analogue of the double Fourier sphere method to form "ballfun" objects. Operations such as function evaluation, differentiation, integration, fast rotation by an Euler angle, and a Helmholtz solver are designed. Our algorithms are particularly efficient for vector calculus operations, and we describe how to compute the poloidal-toroidal and Helmholtz--Hodge decomposition of a vector field defined on the ball.Comment: 23 pages, 9 figure

Fingerprint Recognition: A Histogram Analysis Based Fuzzy C-Means Multilevel Structural Approach

In order to fight identity fraud, the use of a reliable personal identifier has become a necessity. Fingerprints are considered one of the best biometric measurements and are used as a universal personal identifier. There are two main phases in the recognition of personal identity using fingerprints: 1) extraction of suitable features of fingerprints, and 2) fingerprint matching making use of the extracted features to find the correspondence and similarity between the fingerprint images. Use of global features in minutia-based fingerprint recognition schemes enhances their recognition capability but at the expense of a substantially increased complexity. The recognition accuracies of most of the fingerprint recognition schemes, which rely on some sort of crisp clustering of the fingerprint features, are adversely affected due to the problems associated with the behavioral and anatomical characteristics of the fingerprints. The objective of this research is to develop efficient and cost-effective techniques for fingerprint recognition, that can meet the challenges arising from using both the local and global features of the fingerprints as well as effectively deal with the problems resulting from the crisp clustering of the fingerprint features. To this end, the structural information of local and global features of fingerprints are used for their decomposition, representation and matching in a multilevel hierarchical framework. The problems associated with the crisp clustering of the fingerprint features are addressed by incorporating the ideas of fuzzy logic in developing the various stages of the proposed fingerprint recognition scheme. In the first part of this thesis, a novel low-complexity multilevel structural scheme for fingerprint recognition (MSFR) is proposed by first decomposing fingerprint images into regions based on crisp partitioning of some global features of the fingerprints. Then, multilevel feature vectors representing the structural information of the fingerprints are formulated by employing both the global and local features, and a fast multilevel matching algorithm using this representation is devised. Inspired by the ability of fuzzy-based clustering techniques in dealing more effectively with the natural patterns, in the second part of the thesis, a new fuzzy based clustering technique that can deal with the partitioning problem of the fingerprint having the behavioral and anatomical characteristics is proposed and then used to develop a fuzzy based multilevel structural fingerprint recognition scheme. First, a histogram analysis fuzzy c-means (HA-FCM) clustering technique is devised for the partitioning of the fingerprints. The parameters of this partitioning technique, i.e., the number of clusters and the set of initial cluster centers, are determined in an automated manner by employing the histogram of the fingerprint orientation field. The development of the HA-FCM partitioning scheme is further pursued to devise an enhanced HA-FCM (EAH-FCM) algorithm. In this algorithm, the smoothness of the fingerprint partitioning is improved through a regularization of the fingerprint orientation field, and the computational complexity is reduced by decreasing the number of operations and by increasing the convergence rate of the underlying iterative process of the HA-FCM technique. Finally, a new fuzzy based fingerprint recognition scheme (FMSFR), based on the EHA-FCM partitioning scheme and the basic ideas used in the development of the MSFR scheme, is proposed. Extensive experiments are conducted throughout this thesis using a number of challenging benchmark databases. These databases are selected from the FVC2002, FVC2004 and FVC2006 competitions containing a wide variety of challenges for fingerprint recognition. Simulation results demonstrate not only the effectiveness of the proposed techniques and schemes but also their superiority over some of the state-of-the-art techniques, in terms of the recognition accuracy and the computational complexity

Doctor of Philosophy

dissertationWith modern computational resources rapidly advancing towards exascale, large-scale simulations useful for understanding natural and man-made phenomena are becoming in- creasingly accessible. As a result, the size and complexity of data representing such phenom- ena are also increasing, making the role of data analysis to propel science even more integral. This dissertation presents research on addressing some of the contemporary challenges in the analysis of vector fields--an important type of scientific data useful for representing a multitude of physical phenomena, such as wind flow and ocean currents. In particular, new theories and computational frameworks to enable consistent feature extraction from vector fields are presented. One of the most fundamental challenges in the analysis of vector fields is that their features are defined with respect to reference frames. Unfortunately, there is no single ""correct"" reference frame for analysis, and an unsuitable frame may cause features of interest to remain undetected, thus creating serious physical consequences. This work develops new reference frames that enable extraction of localized features that other techniques and frames fail to detect. As a result, these reference frames objectify the notion of ""correctness"" of features for certain goals by revealing the phenomena of importance from the underlying data. An important consequence of using these local frames is that the analysis of unsteady (time-varying) vector fields can be reduced to the analysis of sequences of steady (time- independent) vector fields, which can be performed using simpler and scalable techniques that allow better data management by accessing the data on a per-time-step basis. Nevertheless, the state-of-the-art analysis of steady vector fields is not robust, as most techniques are numerical in nature. The residing numerical errors can violate consistency with the underlying theory by breaching important fundamental laws, which may lead to serious physical consequences. This dissertation considers consistency as the most fundamental characteristic of computational analysis that must always be preserved, and presents a new discrete theory that uses combinatorial representations and algorithms to provide consistency guarantees during vector field analysis along with the uncertainty visualization of unavoidable discretization errors. Together, the two main contributions of this dissertation address two important concerns regarding feature extraction from scientific data: correctness and precision. The work presented here also opens new avenues for further research by exploring more-general reference frames and more-sophisticated domain discretizations