This paper presents an approach for matching latent to rolled fingerprints using the (a) similarity of learned representations of patches and (b) the minutiae on the correlated patches. A deep learning network is used to learn optimized representations of image patches. Similarity scores between patches from the latent and reference fingerprints are determined using a distance metric learned with a convolutional neural network. The matching score is obtained by fusing the patch and minutiae similarity scores.

Latent fingerprints are fingerprint impressions unintentionally left on surfaces at a crime scene. They are crucial in crime scene investigations for making identifications or exclusions of suspects. Determining the quality of latent fingerprint images is crucial to the effectiveness and reliability of matching algorithms. To alleviate the inconsistency and subjectivity inherent in feature markups by latent fingerprint examiners, automatic processing of latent fingerprints is imperative. We propose a deep neural network that predicts the quality of image patches extracted from a latent fingerprint and knits them together to predict the quality of a given latent fingerprint.

Liveness detection is an anti-spoofing technique for dealing with presentation attacks on biometrics authentication systems. Since biometrics are usually visible to everyone, they can be easily captured by a malignant user and replicated to steal someone’s identity. In this paper, the classical binary classification formulation (live/fake) is substituted by a deep metric learning framework that can generate a representation of real and artificial fingerprints and explicitly models the underlying factors that explain their interand intra-class variations. The framework is based on a deep triplet network architecture and consists of a variation of the original triplet loss function. Experiments show that the approach can perform liveness detection in real-time outperforming the state-of-the-art on several benchmark datasets.

Latent fingerprints are fingerprints unintentionally left at a crime scene. Due to the poor quality and often complex image background and overlapping patterns characteristic of latent fingerprint images, separating the fingerprint region-of-interest from complex image background and overlapping patterns was a very challenging problem. Proposed is a latent fingerprint segmentation algorithm based on fractal dimension features and weighted extreme learning machine. Feature vectors were built from the local fractal dimension features and used as input to a weighted extreme learning machine ensemble classifier. The patches were classified into fingerprint and non- fingerprint classes. The experimental results of the proposed approach showed significant improvement in both the false detection rate (FDR) and overall segmentation accuracy compared to existing approaches.

Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. We've built novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations.

We have developed a procedure for the estimation of a small gallery size that can generate the optimal error estimate and its confidence on a large population (relative to the size of the gallery). It uses a generalized two-dimensional prediction model that combines a hypergeometric probability distribution model with a binomial model and also considers the data distortion problem in large populations. Learning is incorporated in the prediction process in order to find the optimal small gallery size and to improve the prediction. The Chernoff and Chebychev inequalities are used as a guide to obtain the small gallery size. Results for the prediction are presented for the NIST-4 fingerprint database.

We present a binomial model to predict both fingerprint verification and identification performance. The match and non-match scores are computed, using the number of corresponding triangles as the match metric, between the query and gallery fingerprints. The match score and non-match score in a binomial prediction model are used to predict the performance on large (relative to the size of the gallery) populations from a small gallery.

Fingerprint matching is still a challenging problem for reliable person authentication because of the complex distortions involved in two impressions of the same finger. In this paper, we propose a fingerprint-matching approach based on genetic algorithms (GA), which tries to find the optimal transformation between two different fingerprints. In order to deal with low-quality fingerprint images, which introduce significant occlusion and clutter of minutiae features, we design a fitness function based on the local properties of each triplet of minutiae. The experimental results on National Institute of Standards and Technology fingerprint database, NIST-4, not only show that the proposed approach can achieve good performance even when a large portion of fingerprints in the database are of poor quality, but also show that the proposed approach is better than another approach, which is based on mean-squared error estimation.

We introduce a fingerprint classification approach based on a novel feature-learning algorithm. Unlike current research for fingerprint classification that generally uses well defined meaningful features, our approach is based on Genetic Programming (GP), which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. Our experimental results show that our approach can find good composite operators to effectively extract useful features.

The performance of a recognition system is usually experimentally determined. Therefore, one cannot predict the peiformance of a recognition system a priori for a new dataset. In this paper, a statistical model to predict the value of k in the rank-k identification rate for a given biometric system is presented. Thus, one needs to search only the topmost k match scores to locate the true match object. A geometrical probability distribution is used to model the number of non match scores present in the set of similarity scores. The model is tested in simulation and by using a public dataset. The model is also indirectly validated against the previously published results. The actual results obtained using publicly available database are very close to the predicted results which validates the proposed model.

Predicting performance of biometrics is an important problem in a real world application. We present a binomial model to predict fingerprint recognition performance. We use a fingerprint identification algorithm to find the number of corresponding triangles as the match and non-match scores. Then we use these similarity scores in a binomial prediction model, which uses small gallery to predict performance on a large population. The results on the entire NIST-4 database show that our model can reasonably predict large population performance.

We present a model-based approach for fingerprints which efficiently retrieves correct hypotheses using novel features of triangles formed by the triplets of minutiae as the basic representation unit. The triangle features that we use are its angles, handedness, type, direction, and maximum side. Experimental results on live-scan fingerprint images of varying quality and NIST special database 4 (NIST-4) show that our indexing approach efficiently narrows down the number of candidate hypotheses in the presence of translation, rotation, scale, shear, occlusion, and clutter.

Due to the complex distortions involved in two impressions of the same finger, fingerprint identification is still a challenging problem. We propose a two step fingerprint identification approach based on the triplets of minutiae. The experimental results on National Institute of Standards and Technology special fingerprint database 4, NIST-4, show that the proposed approach provides a reduction by a factor of 10 for the number of the hypotheses that need to be considered if linear search is used and can achieve a good performance even when a large portion of fingerprints in the database are of poor quality.

We present a comparison of two key approaches for fingerprint identification. These approaches are based on (a) classification followed by verification, and (b) indexing followed by verification. The fingerprint classification approach is based on a novel feature-learning algorithm. It learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. These features are then used for classification of fingerprint into five classes. The indexing approach is based on novel triplets of minutiae. The verification algorithm based on Least Square Minimization over each of the possible triplets minutiae pair is used for identification in both cases. On the NIST-4 fingerprint database, the comparison shows that, although correct classification rate can be as high as 92.8% for 5-class problems, the indexing approach performs better based on size of search space and identification results.

We present a fingerprint classification approach based on a novel feature-learning algorithm. Unlike current research for fingerprint classification that generally uses visually meaningful features, our approach is based on Genetic Programming (GP), which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. Our experimental results show that our approach can find good composite operators to effectively extract useful features. Using a Bayesian classifier, without rejecting any fingerprints from NIST-4, the correct rates for 4 and 5-class classification are 93.2% and 91.2% respectively, which compare favorably and have advantages over the best results published to date.

Fingerprints have long been used for person authentication. However, there is not enough scientific research to explain the probability that two fingerprints, which are impressions of different fingers, may be taken as the same one. We propose a formal framework to estimate the fundamental algorithm independent error rate of fingerprint matching. Unlike a previous work, which assumes that there is no overlap between any two minutiae uncertainty areas and only measures minutiae’s positions and orientations, in our model, we do not make this assumption and measure the relations, i.e. ridge counts, between different minutiae as well as minutiae’s positions and orientations. The error rates of fingerprint matching obtained by our approach are significantly lower than that of previously published research. Results are shown using NIST-4 fingerprint database. These results contribute towards making fingerprint matching a science and settling the legal challenges to fingerprints.

Fingerprint matching is still a challenging problem for reliable person authentication because of the complex distortions involved in two impressions of the same finger. We propose a fingerprint matching approach based on Genetic Algorithms (GA), which finds the optimal global transformation between two different fingerprints. In order to deal with low quality fingerprint images, which introduce significant occlusion and clutter of minutiae features, we de-sign the fitness function based on the local properties of each triplet of minutiae. The experimental results on National Institute of Standards and Technology fingerprint database, NIST-4, not only show that the proposed approach can achieve good performance even when a large portion of fingerprints in the database are of poor quality, but also show that the proposed approach is better than another approach, which is based on mean-squared error estimation.

Fingerprint matching is still a challenging problem for reliable person authentication because of the complex distortions involved in two impressions of the same finger. We propose a fingerprint matching approach based on Genetic Algorithms (GA), which finds the optimal global transformation between two different fingerprints. In order to deal with low quality fingerprint images, which introduce significant occlusion and clutter of minutiae features, we design the fitness function based on the local properties of each triplet of minutiae. The experimental results on National Institute of Standards and Technology fingerprint database, NIST-4, not only show that the proposed approach can achieve good performance even when a large portion of fingerprints in the database are of poor quality, but also show that the proposed approach is better than another approach, which is based on mean-squared error estimation.

In this paper, we propose a fingerprint identification approach based on the triplets of minutiae. The features that we use to find the potential corresponding triangles include angles, triangle orientation, triangle direction, maximum side, minutiae density and ridge counts. False corresponding triangles are eliminated by applying constraints to the transformation between two potential corresponding triangles. The experimental results on National Institute of Standards and Technology special fingerprint database 4, NIST-4, show that, as compared to the linear search, the proposed approach provides a reduction by a factor of 200 for the number of the hypotheses that need to be considered and it can achieve good performance even when a large portion of fingerprints in the database are of poor quality

Most current techniques for minutiae extraction in fingerprint images utilize complex preprocessing and postprocessing. In this paper, we propose a new technique, based on the use of learned templates, which statistically characterize the minutiae. Templates are learned from examples by optimizing a criterion function using Lagrange's method. To detect the presence of minutiae in test images, templates are applied with appropriate orientations to the binary image only at selected potential minutia locations. Several performance measures, which evaluate the quality and quantity of extracted features and their impact on identification, are used to evaluate the signi3cance of learned templates. The performance of the proposed approach is evaluated on two sets of fingerprint images: one is collected by an optical scanner and the other one is chosen from NIST special fingerprint database 4. The experimental results show that learned templates can improve both the features and the performance of the identification system.

This paper presents a model-based approach, which efficiently retrieves correct hypotheses using properties of triangles formed by the triplets of minutiae as the basic representation unit. We show that the uncertainty of minutiae locations associated with feature extraction and shear does not affect the angles of a triangle arbitrarily. Geometric constraints based on characteristics of minutiae are used to eliminate erroneous correspondences. We present an analysis to characterize the discriminating power of our indexing approach. Experimental results on fingerprint images of varying quality show that our approach efficiently narrows down the number of candidate hypotheses in the presence of translation, rotation, scale, shear, occlusion and clutter.

We present a novel approach for extraction of minutiae features from fingerprint images. The proposed approach is based on the use of logical templates for minutiae extraction in the presence of data distortion. A logical template is an expression that is applied to the binary ridge (valley) image at selected potential locations to detect the presence of minutia at these locations. It is adapted to local ridge orientation and frequency. We discuss the proposed technique in detail, and present experimental results on low-resolution images of various qualities.