With identity fraud in our society reaching unprecedented proportions and with an increasing emphasis on the emerging automatic personal identification applications, 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 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 [1]. 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.

Fingerprint verification is an important biometric technique for personal identification. In this paper, we describe the design and implementation of a prototype automatic identity authentication system which uses fingerprints to authenticate the identity of an individual. We have developed an improved minutiae extraction algorithm which is faster and more accurate than our earlier algorithm [58]. 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 adaptively compensate for the nonlinear deformations and inexact transformations between an input and a template. To establish an objective assessment of our system, both the MSU and the NIST 9 fingerprint databases have been used to estimate the performance numbers. The experimental results reveal that our system can achieve a good performance on these databases. ...

The latest research indicates using a combination of biometric avenues for human identification is more effective, and far more challenging.

This work presents two mechanisms for processing fingerprint images; shape-adapted smoothing based on second moment descriptors and automatic scale selection based on normalized derivatives. The shape adaptation procedure adapts the smoothing operation to the local ridge structures, which allows interrupted ridges to be joined without destroying essential singularities such as branching points and enforces continuity of their directional fields. The scale selection procedure estimates local ridge width and adapts the amount of smoothing to the local amount of noise. In addition, a ridgeness measure is defined, which reflects how well the local image structure agrees with a qualitative ridge model, and is used for spreading the results of shape adaptation into noisy areas. The combined approach makes it possible to resolve fine scale structures in clear areas while reducing the risk of enhancing noise in blurred or fragmented areas. The result is a reliable and adaptively detailed estimate of the ridge orientation field and ridge width, as well as a smoothed grey-level version of the input image. We propose that these general techniques should be of interest to developers of automatic fingerprint identification

Automatic fingerprint identification is one of the most important biometric technology. In order to efficiently match fingerprints in a large database, an indexing scheme is necessary. Fingerprint classification, which refers to assigning a fingerprint image into a number of pre-specified classes, provides a feasible indexing mechanism. In practice, however, large intraclass and small interclass variations in global pattern configuration and poor quality of fingerprint images make the classification problem very difficult. A fingerprint classification algorithm requires a robust feature extractor which should be able to reliablely extract salient features from input images. We present a fingerprint classification algorithm with an improved feature extraction algorithm and a novel classification scheme. This algorithm has been tested on the NIST-4 fingerprint database. For the 4,000 images in this database, error rates of 12.5% for the five-class problem and 7.7% for the four-class problem have been achieved. With a 20% reject rate (which eliminates most of the poor quality images in the database), the error of the four-class problem drops to 2.4%.

Introduction More than a century has passed since Alphonse Bertillon first conceived and then industriously practiced the idea of using body measurements for solving crimes [1]. Just as his idea was gaining popularity, it faded into relative obscurity by a far more significant and practical discovery of the uniqueness of the human fingerprints 1 . Soon after this discovery, many major law enforcement departments embraced the idea of first "booking" the fingerprints of criminals, so that their records are readily available and later using leftover fingerprint smudges (latents), the identity of criminals can be determined. These agencies sponsored a rigorous study of fingerprints, developed scientific 1 In 1893, the Home Ministry Office, UK, accepted that no two individuals have the same fingerprints. 1 methods for visual matching of fingerprints and strong programs/cultures for training fingerprint experts, and applied the art

Contrary to popular belief, despite decades of research in fingerprints, reliable fingerprint recognition is an open problem. Extracting features out of poor quality prints is the most challening problem faced in this area. This paper introduces a new approach for fingerprint enhancement based on Short Time Fourier Transform(STFT) Analysis. STFT is a well known technique to analyze non-stationary signals. We extend its application to 2D images. The algorithm simultaneously estimates all the intrinsic properties of the fingerprints such as the foreground region mask, local ridge orientation and local frequency orientation. Furthermore we propose a probabilistic approach of robustly estimating these parameters. We compare the proposed approach to other filtering approaches and show that our technique performs favorably. We also objectively measure the improvement in recognition rate due to our enhancement. We obtain a 17 % improvement in the recognition rate on a set of 800 images from the FVC2002 database.

Fingerprints are widely used in automatic identity verification systems. The core of such systems is the verification algorithm to match two fingerprints. So far, various method for fingerprint matching have been proposed, but few works investigated the fusion of two or more matching algorithms. In this paper, various methods for fusing such algorithms have been investigated. Experimental results showed that such fusion can outperform the best individual verification algorithm and increase the discrimination between genuine and impostor classes.

A few works have been presented so far on information fusion for fingerprint verification. None, however, have explicitly investigated the use of multi-sensor fusion, in other words, the integration of the information provided by multiple devices to capture fingerprint images. In this paper, a multi-sensor fingerprint verification system based on the fusion of optical and capacitive sensors is presented. Reported results show that such a multi-sensor system can perform better than traditional fingerprint matchers based on a single sensor.

Fingerprint Classification and Matching Using a Filterbank By Salil Prabhakar Accurate automatic personal identification is critical in a variety of applications in our electronically interconnected society. Biometrics, which refers to identification based on physical or behavioral characteristics, is being increasingly adopted to provide positive identification with a high degree of confidence. Among all the biometric techniques, fingerprint-based authentication systems have received the most attention because of the long history of fingerprints and their extensive use in forensics. However, the numerous fingerprint systems currently available still do not meet the stringent performance requirements of several important civilian applications. To assess the performance limitations of popular minutiae-based fingerprint verification system, we theoretically estimate the probability of a false correspondence between two fingerprints from di#erent fingers based on the minutiae representation of fingerprints. Due to the limited amount of information present in the minutiae-based representation, it is desirable to explore alternative representations of fingerprints. We present a novel filterbank-based representation of fingerprints. We have used this compact representation for fingerprint classification as well as fingerprint verification. Experimental results show that this algorithm competes well with the state-of-theart minutiae-based matchers. We have developed a decision level information fusion framework which improves the fingerprint verification accuracy when multiple matchers, multiple fingers of the user, or multiple impressions of the same finger are combined. A feature verification and purification scheme is proposed to improve the performance of the minutiae-...