In this paper, we introduce the Minutia Cylinder-Code (MCC): a novel representation based on 3D data structures (called cylinders), built from minutiae distances and angles. The cylinders can be created starting from a subset of the mandatory features (minutiae position and direction) defined by standards like ISO/IEC 19794-2 (2005). Thanks to the cylinder invariance, fixed-length, and bit-oriented coding, some simple but very effective metrics can be defined to compute local similarities and to consolidate them into a global score. Extensive experiments over FVC2006 databases prove the superiority of MCC with respect to three well-known techniques and demonstrate the feasibility of obtaining a very effective (and interoperable) fingerprint recognition implementation for light architectures.

Minutia Cylinder-Code: A New Representation and Matching Technique for Fingerprint Recognition

Where possible, automation has been a common response of humankind to many activities that have to be repeated numerous times. The routine identification of specimens of previously described species has many of the characteristics of other activities that have been automated, and poses a major constraint on studies in many areas of both pure and applied biology. In this paper, we consider some of the reasons why automated species identification has not become widely employed, and whether it is a realistic option, addressing the notions that it is too difficult, too threatening, too different or too costly. Although recognizing that there are some very real technical obstacles yet to be overcome, we argue that progress in the development of automated species identification is extremely encouraging that such an approach has the potential to make a valuable contribution to reducing the burden of routine identifications. Vision and enterprise are perhaps more limiting at present than practical constraints on what might possibly be achieved.

/pdf/automated-species-identification-why-not-3q3nh3ozor.pdf

Automated species identification: why not?

A survey on fingerprint minutiae-based local matching for verification and identification

Fingerprint recognition is one of the most popular and successful methods used for person identification, which takes advantage of the fact that the fingerprint has some unique characteristics called minutiae; which are points where a curve track finishes, intersect with other track or branches off. Biometric identification systems using fingerprints patterns are called AFIS (Automatic Fingerprint Identification System). In this paper a novel method for Fingerprint recognition is considered using a combination of Fast Fourier Transform (FFT) and Gabor Filters to enhancement the fingerprint image was captured using a UareU 4000 fingerprint reader of Digital Person, Inc.

Fingerprint Recognition

Fingerprints have been an invaluable tool for law enforcement and forensics for over a century, motivating research into automated fingerprint-based identification in the early 1960s. More recently, fingerprints have found an application in biometric systems. Biometrics is the automatic identification of an individual based on physiological or behavioural characteristics. Due to its security-related applications and the current world political climate, biometrics is presently the subject of intense research by private and academic institutions. Fingerprints are emerging as the most common and trusted biometric for personal identification. The main objective of this paper is to review the extensive research that has been done on automated fingerprint matching over the last four decades. In particular, the focus is on minutiae-based algorithms. Minutiae features contain most of a fingerprint’s individuality, and are consequently the most important fingerprint feature for verification systems. Minutiae extraction, matching algorithms, and verification performance are discussed in detail, with open problems and future directions identified.

Fingerprint verification based on minutiae features: a review

This paper introduces a robust fingerprint matching scheme based on the comprehensive minutia and the binary relation between minutiae. In the method, a fingerprint is represented as a graph, of which the comprehensive minutiae act as the vertex set and the local binary minutia relations provide the edge set. Then, the transformation-invariant and transformation-variant features are extracted from the binary relation. The transformation-invariant features are suitable to estimate the local matching probability, whereas the transformation-variant features are used to model the fingerprint rotation transformation with the adaptive Parzen window. Finally, the fingerprint matching is conducted with the variable bounded box method and iterative strategy. The experiments demonstrate that the proposed scheme is effective and robust in fingerprint alignment and matching.

/pdf/modeling-and-analysis-of-local-comprehensive-minutia-2dfd3lnxgs.pdf

Modeling and Analysis of Local Comprehensive Minutia Relation for Fingerprint Matching

The invention discloses a method and a system for authenticating identities of a remote network on the basis of multi-biometric features, the system comprises a multi-biometric features network sampling and controlling terminal, the remote network and a central authenticating server, the method comprises building communication connections by the multi-biometric features network sampling and controlling terminal and the central authenticating server, obtaining biometric features data through a biometric features sampling module, encrypting the biometric features data through a communication module, then, transferring to the central authenticating server through the remote network according to a TCP/IP protocol, matching the biometric features data with the biometric features data which is stored in the server by the central authenticating server to authenticate identities of biometric features holders, and sending control orders to the multi-biometric features network sampling and controlling terminal according to authenticating results. The invention enables a computer to be connected with a plurality of sampling equipment which is provided with biometric features as fingerprints,human faces, irises and retinas through the remote network in a mode of the central authenticating server.

Method and system of remote network identification authenticating based on multiple biology characteristics

This paper introduces a probabilistic formulation in terms of Maximum-likelihood estimation to calculate the optimal deformation parameters, such as scale, rotation and translation, between a pair of fingerprints acquired by different image capturers from the same finger. This uncertainty estimation technique allows parameter selection to be performed by choosing parameters that minimize the deformations uncertainty and maximize the global similarity between the pair of fingerprints. In addition, we use a multi-resolution search strategy to calculate the optimal deformation parameters in the space of possible deformation parameters. We apply the method to fingerprint matching in a pension fund management system in China, a fingerprint-based personal identification application system. The performance of the method shows that it is effective in estimating the optimal deformation parameters between a pair of fingerprints.

Maximum-likelihood deformation analysis of different-sized fingerprints

In this paper, a new method based on relative difference space (RDS) and support vector machine (SVM) is proposed for multi-class recognition. First the RDS transformation converts the multi-class problem to a binary-class problem, and then SVM is used for the binary classification directly. Compared with the traditional method of difference space (DS), RDS is reversible and it overcomes the ill-transformation problem. This method is applied to face recognition in Yale Face database B, and the recognition result demonstrates its robust performance under different illumination conditions

Face Recognition with Relative Difference Space and SVM

The utility model discloses a telenet identity authenticating system which is based on the multi-biometric character. The system includes a multi-biometric character network collecting and controlling terminal, a telenet, and a central authenticating server. The multi-biometric character network collecting and controlling terminal establishes a communication connection with the central authenticating server, acquires the biometric character data by a biometric character collecting module, and transfers the biometric character data to the central authenticating server through the communication module in the TCP/IP protocol. The central authenticating server matches the biometric character data with the biometric character data which is stored in the server in order to authenticate the identity of the biometric character holder, and sends control instruction to the multi-biometric character network collecting and controlling terminal according to the authenticated result. The utility model enables a computer to be connected to a fingerprint, face, iris, and retina multi-biometric character collecting device through the telenet in the method of the central authenticating server.

Multi-biological characteristic based remote network identification authentication system

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