A Fast Minutiae-Based Fingerprint Recognition System

The spectral minutiae representation is a method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require as an input a fixed-length feature vector. Based on the spectral minutiae features, this paper introduces two feature reduction algorithms: the Column Principal Component Analysis and the Line Discrete Fourier Transform feature reductions, which can efficiently compress the template size with a reduction rate of 94%. With reduced features, we can also achieve a fast minutiae-based matching algorithm. This paper presents the performance of the spectral minutiae fingerprint recognition system and shows a matching speed with 125 000 comparisons per second on a PC with Intel Pentium D processor 2.80 GHz and 1 GB of RAM. This fast operation renders our system suitable as a preselector for a large-scale fingerprint identification system, thus significantly reducing the time to perform matching, especially in systems operating at geographical level (e.g., police patrolling) or in complex critical environments (e.g., airports)

Spectral Minutiae Fingerprint Recognition System

Biometrics refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics, such as faces, finger prints, iris, and gait. In this paper, we focus on the application of finger print recognition system. The spectral minutiae fingerprint recognition is a method to represent a minutiae set as a fixedlength feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. Based on the spectral minutiae features, this paper introduces two feature reduction algorithms: the Column Principal Component Analysis and the Line Discrete Fourier Transform feature reductions, which can efficiently compress the template size with a reduction rate of 94%.With reduced features, we can also achieve a fast minutiae-based matching algorithm. This paper presents the performance of the spectral minutiae fingerprint recognition system, this fast operation renders our system suitable for a large-scale fingerprint identification system, thus significantly reducing the time to perform matching, especially in systems like, police patrolling, airports etc,. The spectral minutiae representation system tends to significantly reduce the false acceptance rate with a marginal increase in the false rejection rate

Spectral minutiae representations for fingerprint recognition

The term biometrics refers to the technologies that measure and analyze human intrinsic physical or behavioral characteristics for authenticating individuals. Nowadays, biometric technology is increasingly deployed in civil and commercial applications. The growing use of biometrics is raising security and privacy concerns. Storing biometric data, known as biometric templates, in a database leads to several privacy risks such as identity fraud and cross matching. A solution is to apply biometric template protection techniques, which aim to make it impossible to recover the biometric data from the templates.\ud The goal of our research is to combine biometric systems with template protection. Aimed at fingerprint recognition, this thesis introduces the Spectral Minutiae Representation method, which enables the combination of a minutiae-based fingerprint recognition system with template protection schemes based on fuzzy commitment or helper data schemes.\ud In this thesis, three spectral minutiae representation methods have been proposed: the location-based spectral minutiae representation (SML), the orientation-based spectral minutiae representation (SMO) and the complex spectral minutiae representation (SMC). From the experiments shown in this thesis, SMC achieved the best results.\ud Based on the spectral minutiae features, this thesis further presented contributions in three research directions. First, this thesis recommends several ways to enhance the recognition performance of SMC. Second, with regard to feature reduction, this thesis introduced two feature reduction methods, Column-PCA (CPCA) and Line-DFT (LDFT). Third, with regard to quantization, this thesis introduced the Spectral Bits and Phase Bits representations. \ud The spectral minutiae representation scheme proposed in this thesis enables the combination of fingerprint recognition systems with template protection based on the helper data scheme. Furthermore, this scheme allows for a fast minutiae comparison, which renders this scheme suitable as a pre-selector for a large-scale fingerprint identification system, thus significantly reducing the time to perform matching. The binary spectral minutiae representation achieved an equal error rate of less than 1% on the FVC2000-DB2 database when applying multi-sample enrolment. The fast comparison speed together with the promising recognition performance makes this spectral minutiae scheme very applicable for real time applications

Fingerprint recognition system to verify the identity of a person using an online database

Our B.Tech project emphasize on the current techniques for the fingerprint recognition. Human fingerprint exhibit some certain details marked on it. We categorized it as minutiae, which can be used as a unique identity of a person if we recognize it in a well manner. The main aim of this project is to design a complete system and an indigenous design model for fingerprint verification from an online database using minutiae matching technique. So, in order to have a good quality minutiae extraction the fingerprint image is first pre-processed by image enhancement which includes histogram equalization, Fast Fourier Transform and binerization and then segmentation is done to get the effective area of the fingerprint followed by minutiae extraction which includes ridge thinning and minutiae marking and then we have a post-processing operation which includes removal of H-breaks, isolated points and false minutiae. Now, we go for a final treatment which is ‘minutiae matching’, in minutiae matching we match the post-processed fingerprint image with the online database. For all these operations, we develop an alignment based matching algorithm which is for minutiae matching. This algorithm has a specialty that it itself finds the correspondences between input minutiae and the stored template minutiae pattern and there is no resorting to exhaustive search. We can then evaluate the performance of the system on a database by taking fingerprints of different people

Spectral representation of fingerprints

Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and directions suffering from various deformations such as translation, rotation and scaling. The spectral minutiae representation introduced in this paper is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with a template protection scheme, which requires a fixed-length feature vector. This paper introduces the idea and algorithm of spectral minutiae representation. A correlation based spectral minutiae\ud matching algorithm is presented and evaluated. The scheme shows a promising result, with an equal error rate of 0.2% on manually extracted minutiae