A double-identity fingerprint is a fake fingerprint created by combining features from two different fingers, so that it has a high chance to be falsely matched with fingerprints from both fingers. This paper studies the feasibility of creating double-identity fingerprints by proposing two possible techniques and evaluating to what extent they may be used to fool the state-of-the-art fingerprint recognition systems. The results of systematic experiments suggest that existing algorithms are highly vulnerable to this specific attack (about 90% chance of success at FAR = 0.1%) and that the fingerprint patterns generated might be realistic enough to fool human examiners.

This paper introduces a novel method for the generation of high-resolution synthetic hand-print images. Specific traits, such as fingerprint, palmprint, and hand-shape, are synthesized to obtain a whole hand-print. Each trait is generated by a methodology that mimics the nature of the corresponding biometric data and their main degrees of freedom. The biometric traits are then integrated into a single high-resolution realistic image. A quantitative validation of the obtained patterns is carried out in the context of minutiae matching by comparing genuine and impostor distributions between synthetic and real hand-prints. The proposed approach also proved to be useful for algorithm training/optimization.

Although several fingerprint template protection methods have been proposed in the literature, the problem is still unsolved, since enforcing nonreversibility tends to produce an excessive drop in accuracy. Furthermore, unlike fingerprint verification, whose performance is assessed today with public benchmarks and protocols, performance of template protection approaches is often evaluated in heterogeneous scenarios, thus making it very difficult to compare existing techniques. In this paper, we propose a novel protection technique for Minutia Cylinder-Code (MCC), which is a well-known local minutiae representation. A sophisticate algorithm is designed to reverse MCC (i.e., recovering original minutiae positions and angles). Systematic experimentations show that the new approach compares favorably with state-of-the-art methods in terms of accuracy and, at the same time, provides a good protection of minutiae information and is robust against masquerade attacks.

Palmprint recognition is a challenging problem, mainly due to low quality of the pattern, large nonlinear distortion between different impressions of the same palm and large image size, which makes feature extraction and matching computationally demanding. This paper introduces a high-resolution palmprint recognition system based on minutiae. The proposed system follows the typical sequence of steps used in fingerprint recognition, but each step has been specifically designed and optimized to process large palmprint images with a good tradeoff between accuracy and speed. A sequence of robust feature extraction steps allows to reliably detect minutiae; moreover, the matching algorithm is very efficient and robust to skin distortion, being based on a local matching strategy and an efficient and compact representation of the minutiae. Experimental results show that the proposed system compares very favorably with the state of the art.

This paper summarizes the results of the fingerprint verification competition organized in conjunction with IJCB 2011. The competition focused on benchmarks covering both proprietary encoding and ISO template format. Considering the benchmarks difficulty, some of the algorithms submitted achieved very good accuracy: a 0.7% EER and a 1.1% EER were obtained on two challenging benchmarks, using proprietary and ISO template formats, respectively. Based on the participant self-description of the best performing algorithms we tried to figure out the most promising building-block technologies.

This paper proposes a new hash-based indexing method to speed up fingerprint identification in large databases. A Locality-Sensitive Hashing (LSH) scheme has been designed relying on Minutiae Cylinder-Code (MCC), which proved to be very effective in mapping a minutiae-based representation (position/ angle only) into a set of fixed-length transformation-invariant binary vectors. A novel search algorithm has been designed thanks to the derivation of a numerical approximation for the similarity between MCC vectors. Extensive experimentations have been carried out to compare the proposed approach against 15 existing methods over all the benchmarks typically used for fingerprint indexing. In spite of the smaller set of features used (top performing methods usually combine more features), the new approach outperforms existing ones in almost all of the cases.

This correspondence proposes new candidate list reduction criteria for fingerprint indexing approaches. The basic idea is that, given a query fingerprint, the initial set of scores produced by an indexer could contain useful information to reduce the candidate list. Novel reduction criteria have been proposed, and extensive experiments have been carried out over five publicly available benchmarks, using two state-of-the-art fingerprint indexing techniques. Although quite simple, the proposed criteria achieved remarkable results, allowing a substantial reduction of the candidate list: for instance, at 1% error rate, the average penetration rate of a state-of-the-art minutiae-based indexer decreases from 27% to 3.9% on FVC2000 DB2. The new reduction criteria are applicable to any indexing approach, since they only require a list of scores as input.

Computation of local orientations is a primary step in fingerprint recognition. A large number of approaches have been proposed in the literature, but no systematic quantitative evaluations have been done yet. We implemented and tested several well know methods and a plethora of their variants over a novel, specifically designed, benchmark, made available in the FVC-onGoing framework. We proved that parameter optimizations, pre- and post-processing stages can markedly improve accuracy of the baseline methods on bad quality fingerprints. Finally, in this paper we propose a novel adaptive method which selectively exploits accuracy of local-based analysis and learning-based global methods, thus achieving the overall best performance on a challenging dataset.

This paper describes a new fingerprint indexing approach based on vector and scalar features, obtained from ridge-line orientations and frequencies. A carefully designed set of features and ad-hoc score measures allow the proposed indexing algorithm to be extremely effective and efficient, as confirmed by the results of extensive experiments. The new method markedly outperforms competing state-of-the-art techniques over six publicly available data sets. Furthermore, it can scale to large databases without losing accuracy: on a standard PC, a search over one million fingerprints takes less than 1 s.

This paper describes an improved version of the MCC fingerprint matching approach. An in-depth error analysis allowed us to point out the weakest points of the original MCC and to design: i) a more effective minutiae pair selection and ii) a more distortion-tolerant relaxation. The parameters of the new version have been tuned over a new larger dataset and the final algorithm has been evaluated on FVC-onGoing. The results show that MCC compares favorably with some of the most accurate commercial algorithms published in FVC-onGoing.

The computation of local orientations is a fundamental step in fingerprint recognition. Although a large number of approaches have been proposed in the literature, no systematic quantitative evaluations have been done yet, mainly due to the lack of proper datasets with associated ground truth information. In this paper we propose a new benchmark (which includes two datasets and an accuracy metric) and report preliminary results obtained by testing four well-known local orientation extraction algorithms.

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.

Fingerprint singularities play an important role in several fingerprint recognition and classification systems. Although some general relationships and constraints about the location of singularities in the different fingerprint classes are well known, to the best of our knowledge no statistical models have been developed until now. This paper studies the spatial distributions of singularity locations in nature and derives, from a representative dataset of labelled samples, the probability density functions of the four main fingerprint classes. The results obtained can be directly exploited to improve the accuracy of many techniques relying on the position of singularities, as confirmed by the results of two experiments on fingerprint classification and synthesis.

This work reports a preliminary study on the vulnerability evaluation of fingerprint verification systems to direct attacks carried out with fake fingertips created from minutiae templates. The attack is performed by presenting to the acquisition sensor a fake fingertip generated from an image reconstructed from a compromised minutiae-based template. Experiments carried out against a state-of-the-art fingerprint recognition algorithm show that the proposed attack scheme is definitely feasible and highlight a potential security threat in the use of non-encrypted minutiae-based templates.

This paper addresses the problem of evaluating the ldquooperational qualityrdquo of fingerprint scanners, that is, the ability of acquiring images that maximize the accuracy of automated fingerprint recognition. The quality parameters commonly used to quantify the fidelity of a scanner in sensing the input pattern have been analyzed and a large experimentation has been carried out to understand their effects on fingerprint recognition accuracy. The experimental results show that some parameters have a strong impact, while others appear to be less relevant.

A minutiae-based template is a very compact representation of a fingerprint image and for a long time it has been assumed that it did not contain enough information to allow the reconstruction of the original fingerprint. This work proposes a novel approach to reconstruct fingerprint images from standard templates and investigates to what extent the reconstructed images are similar to the original ones (i.e., those the templates were extracted from). The efficacy of the reconstruction technique has been assessed by estimating the success chances of a masquerade attack against nine different fingerprint recognition algorithms. The experimental results show that the reconstructed images are very realistic and that, although it is unlikely they can fool a human expert, there is a high chance to deceive state-of-the-art commercial fingerprint recognition systems.

For a long time it has been assumed that a minutiae-based fingerprint template did not contain enough information to allow reconstructing the original fingerprint image. This work proposes an approach to reconstruct fingerprint images from ISO standard templates and investigates to what extent the reconstructed fingerprints are similar to the original ones. The results show that the reconstructed images have a very good quality and may be used to attack existing fingerprint recognition systems. Systematic studies to understand the success chances of such attacks are currently being carried out

This paper is concerned with the performance evaluation of fingerprint verification systems. After an initial classification of biometric testing initiatives, we explore both the theoretical and practical issues related to performance evaluation by presenting the outcome of the recent Fingerprint Verification Competition (FVC2004). FVC2004 was organized by the authors of this work for the purpose of assessing the state-of-the-art in this challenging pattern recognition application and making available a new common benchmark for an unambiguous comparison of fingerprint-based biometric systems. FVC2004 is an independent, strongly supervised evaluation performed at the evaluators' site on evaluators' hardware. This allowed the test to be completely controlled and the computation times of different algorithms to be fairly compared. The experience and feedback received from previous, similar competitions (FVC2000 and FVC2002) allowed us to improve the organization and methodology of FVC2004 and to capture the attention of a significantly higher number of academic and commercial organizations (67 algorithms were submitted for FVC2004). A new, "Light" competition category was included to estimate the loss of matching performance caused by imposing computational constraints. This paper discusses data collection and testing protocols, and includes a detailed analysis of the results. We introduce a simple but effective method for comparing algorithms at the score level, allowing us to isolate difficult cases (images) and to study error correlations and algorithm "fusion." The huge amount of information obtained, including a structured classification of the submitted algorithms on the basis of their features, makes it possible to better understand how current fingerprint recognition systems work and to delineate useful research directions for the future.

Attacking fingerprint-based biometric systems by presenting fake fingers at the sensor could be a serious threat for unattended applications. This work introduces a new approach for discriminating fake fingers from real ones, based on the analysis of skin distortion. The user is required to move the finger while pressing it against the scanner surface, thus deliberately exaggerating the skin distortion. Novel techniques for extracting, encoding and comparing skin distortion information are formally defined and systematically evaluated over a test set of real and fake fingers. The proposed approach is privacy friendly and does not require additional expensive hardware besides a fingerprint scanner capable of capturing and delivering frames at proper rate. The experimental results indicate the new approach to be a very promising technique for making fingerprint recognition systems more robust against fake-finger-based spoofing attempts.

This paper introduces a new noise model for the generation of synthetic fingerprints. Starting from several visual observations about noise in real fingerprints, the limits of the uniform noise generation adopted in our previous work are pointed out and a new model able to create realistic coherent noise is proposed. The results are very promising: the new images generated are definitely more realistic than those obtained with the original method.

This paper introduces a new face recognition approach that allows face variations (produced by aging and other appearance changes) to be dealt with. During the initial learning, a set of MKL subspaces is created for each individual, starting from the feature vectors extracted through a bank of Gabor filters. Then, during the normal system operation, an incremental updating technique can be applied to adjust the subspaces without recalculating the face models from scratch; this makes the method able to cope with gradual changes that occur over time. The results of the experimentation performed on three face databases prove the advantages of the proposed approach with respect to other well-known techniques; in particular, our method achieves better accuracy and higher robustness against face variations.

Reliable and accurate fingerprint recognition is a challenging pattern recognition problem, requiring algorithms robust in many contexts. FVC2000 competition attempted to establish the first common benchmark, allowing companies and academic institutions to unambiguously compare performance and track improvements in their fingerprint recognition algorithms. Three databases were created using different state-of-the-art sensors and a fourth database was artificially generated; 11 algorithms were extensively tested on the four data sets. We believe that FVC2000 protocol, databases, and results will be useful to all practitioners in the field not only as a benchmark for improving methods, but also for enabling an unbiased evaluation of algorithms.

This work introduces the multispace Karhunen-Loeve (MKL) as a new approach to unsupervised dimensionality reduction for pattern representation and classification. The training set is automatically partitioned into disjoint subsets, according to an optimality criterion; each subset then determines a different KL subspace which is specialized in representing a particular group of patterns. The extension of the classical KL operators and the definition of ad hoc distances allow MKL to be effectively used where KL is commonly employed. The limits of the standard KL transform are pointed out, in particular, MKL is shown to outperform KL when the data distribution is far from a multidimensional Gaussian and to better cope with large sets of patterns, which could cause a severe performance drop in KL.

Introduces a method for the generation of synthetic fingerprint images. Gabor-like space-variant filters are used for iteratively expanding an initially empty image containing just one or a few seeds. A directional image model, whose inputs are the number and location of the fingerprint cores and deltas, is used for tuning the filters according to the underlying ridge orientation. Very realistic fingerprint images are obtained after the final noising-and-rendering stage.

In this work, we introduce a new approach to automatic fingerprint classification. The directional image is partitioned into "homogeneous" connected regions according to the fingerprint topology, thus giving a synthetic representation which can be exploited as a basis for the classification. A set of dynamic masks, together with an optimization criterion, are used to guide the partitioning. The adaptation of the masks produces a numerical vector representing each fingerprint as a multidimensional point, which can be conceived as a continuous classification. Different search strategies are discussed to efficiently retrieve fingerprints both with continuous and exclusive classification. Experimental results have been given for the most commonly used fingerprint databases and the new method has been compared with other approaches known in the literature: As to fingerprint retrieval based on continuous classification, our method gives the best performance and exhibits a very high robustness.