A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or, simply, biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics, it is possible to confirm or establish an individual's identity based on "who she is", rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). We give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.

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An introduction to biometric recognition

A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators

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Handbook of Fingerprint Recognition

This article discusses the challenges in computer systems research posed by the emerging field of pervasive computing. It first examines the relationship of this new field to its predecessors: distributed systems and mobile computing. It then identifies four new research thrusts: effective use of smart spaces, invisibility, localized scalability, and masking uneven conditioning. Next, it sketches a couple of hypothetical pervasive computing scenarios, and uses them to identify key capabilities missing from today's systems. The article closes with a discussion of the research necessary to develop these capabilities.

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Pervasive computing: vision and challenges

In order to ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of input fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. We present a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency. We have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the verification accuracy.

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Fingerprint image enhancement: algorithm and performance evaluation

https://www.cse.msu.edu/~rossarun/pubs/RossScoreNormalization_PR05.pdf

Score normalization in multimodal biometric systems

Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is incapable of meeting today's increasing performance requirements. An automatic fingerprint identification system (AFIS) is needed. This paper describes the design and implementation of an online fingerprint verification system which operates in two stages: minutia extraction and minutia matching. An improved version of the minutia extraction algorithm proposed by Ratha et al. (1995), which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an online inkless scanner. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and the stored template without resorting to exhaustive search and has the ability of adaptively compensating for the nonlinear deformations and inexact pose transformations between fingerprints. The system has been tested on two sets of fingerprint images captured with inkless scanners. The verification accuracy is found to be acceptable. Typically, a complete fingerprint verification procedure takes, on an average, about eight seconds on a SPARC 20 workstation. These experimental results show that our system meets the response time requirements of online verification with high accuracy.

On-line fingerprint verification

Biometrics-based verification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a considerable fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically. The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. Further, minutiae-based matching has difficulty in quickly matching two fingerprint images containing a different number of unregistered minutiae points. The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature. Our system performs better than a state-of-the-art minutiae-based system when the performance requirement of the application system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary (minutiae-based and filter-based) fingerprint information.

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Filterbank-based fingerprint matching

Fingerprint verification is an important biometric technique for personal identification. We describe the design and implementation of a prototype automatic identity-authentication system that uses fingerprints to authenticate the identity of an individual. We have developed an improved minutiae-extraction algorithm that is faster and more accurate than our earlier algorithm (1995). An alignment-based minutiae-matching algorithm has been proposed. This algorithm is capable of finding the correspondences between input minutiae and the stored template without resorting to exhaustive search and has the ability to compensate adaptively for the nonlinear deformations and inexact transformations between an input and a template. To establish an objective assessment of our system, both the Michigan State University and the National Institute of Standards and Technology NIST 9 fingerprint data bases have been used to estimate the performance numbers. The experimental results reveal that our system can achieve a good performance on these data bases. We also have demonstrated that our system satisfies the response-time requirement. A complete authentication procedure, on average, takes about 1.4 seconds on a Sun ULTRA I workstation (it is expected to run as fast or faster on a 200 HMz Pentium).

/pdf/an-identity-authentication-system-using-fingerprints-4fq3gtv52o.pdf

An identity-authentication system using fingerprints

W A LT ER S IP SE R For this reason, more and more organizations are looking to automated identity authentication systems to improve customer satisfaction and operating efficiency as well as to save critical resources (see Figure 1). Furthermore, as people become more connected electronically, the ability to achieve a highly accurate automatic personal identification system is substantially more critical [5]. Personal identification is the process of associating a particular individual with an identity. Identification can be in the form of verification (also known as authentication), which entails authenticating a claimed identity (“Am I who I claim I am?”), or recognition (also known as identification), which entails determining the identity of a given person from a database of persons known to the system (“Who am I?”). Knowledge-based and token-based automatic personal identification approaches have been the two traditional techniques widely used [8]. Token-based approaches use something you have to make a personal identification, such as a passport, driver’s license, ID card, credit card, or keys. Knowledge-based approaches use something you know to make a personal identification, such as a password or a personal identification number (PIN). Since these traditional approaches are not based on any inherent attributes of an individual to make a personal identification, they suffer from the

Lin Hong

Papers

Fingerprint image enhancement: algorithm and performance evaluation

On-line fingerprint verification

Filterbank-based fingerprint matching

An identity-authentication system using fingerprints

Biometric identification