Visualization and Intelligent Systems Laboratory
VISLab

 

 

Contact Information

VISLab
Winston Chung Hall Room 216
University of California, Riverside
900 University Avenue
Riverside, CA 92521-0425


Tel: (951)-827-3954

CRIS
Bourns College of Engineering
UCR
NSF IGERT on Video Bioinformatics

UCR Collaborators:
CSE
ECE
ME
STAT
PSYC
ENTM
BIOL
BPSC
ECON
MATH
BIOENG
MGNT

Other Collaborators:
Keio University

Other Activities:
IEEE Biometrics Workshop 2014
IEEE Biometrics Workshop 2013
Worshop on DVSN 2009
Multibiometrics Book

Webmaster Contact Information:
Michael Caputo
michael.vislab@gmail.com

Last updated: June 15, 2016

 

 

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Predicting Models for Multibiometric Systems

An example of feature extraction process; thinned image. 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.

Prediction of Recognition Performance on Large Populations

An example of feature extraction process; thinned image. 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.

Predicting Fingerprint Biometrics Performance from a Small Gallery

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 Classification Based on Learned Features

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.

Prediction and Validation of Indexing Performance for Biometrics

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.

Fingerprint Matching by Genetic Algorithms

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.

Predicting Fingerprint Recognition Performance from a Small Gallery

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.

Fingerprint Indexing Based on Novel Features of Minutiae Triplets

Sample Fingerprint Images. 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.

A Robust Two Step Approach for Fingerprint Identification

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.

Fingerprint Identification: Classification vs. Indexing

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.

Learning Features for Fingerprint Classification

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.

On The Fundamental Performance For Fingerprint Matching

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 Verification Using Genetic Algorithms

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.

Logical Templates for Feature Extraction in Fingerprint Images

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.