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Author

Nalini K. Ratha

Bio: Nalini K. Ratha is an academic researcher from IBM. The author has contributed to research in topics: Biometrics & Fingerprint recognition. The author has an hindex of 50, co-authored 216 publications receiving 12290 citations. Previous affiliations of Nalini K. Ratha include Michigan State University & University at Buffalo.


Papers
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Proceedings ArticleDOI
23 Aug 2004
TL;DR: This work estimates the false reject rates and false accept rates of a biometric authentication system using a real fingerprint dataset and demonstrates that the resampling the subsets of the data samples may be one way of replicating interdependence among the data; the bootstrapping methods using such subset resampled may indeed improve the accuracy of the estimates.
Abstract: Reporting the accuracy performance of pattern recognition systems (e.g., biometrics ID system) is a controversial issue and perhaps an issue that is not well understood. This work focuses on the research issues related to the oft used confidence interval metric for performance evaluation. Using a biometric (fingerprint) authentication system, we estimate the false reject rates and false accept rates of the system using a real fingerprint dataset. We also estimate confidence intervals of these error rates using a number of parametric and non-parametric (e.g., bootstrapping) methods. We attempt to assess the accuracy of the confidence intervals based on estimate and verify strategy applied to repetitive random train/test splits of the dataset. Our experiments objectively verify the hypothesis that the traditional bootstrap and parametric estimate methods are not very effective in estimating the confidence intervals and magnitude of interdependence among data may be one of the reasons for their ineffective estimates. Further, we demonstrate that the resampling the subsets of the data samples (inspired from moving block bootstrap) may be one way of replicating interdependence among the data; the bootstrapping methods using such subset resampling may indeed improve the accuracy of the estimates. Irrespective of the method of estimation, the results show that the (1-/spl alpha/) 100% confidence intervals empirically estimated from the training set capture significantly smaller than (1-/spl alpha/) fraction of the estimates obtained from the test set.

8 citations

Proceedings ArticleDOI
01 Dec 2017
TL;DR: A single use biometric token that relies on Shamir's secret sharing algorithm and blockchain technology to ensure that the encrypted biometric template contained in the token is secure, tamper-proof, and any attempt to use the issued token is irrefutably logged to prove subsequently that the user indeed availed the service.
Abstract: Numerous applications require users to interact with a multitude of entities in order to avail a service. In such applications, the identity of the user is typically verified through physical or digital tokens, which are prone to both identity theft (lost or stolen tokens) and repudiation claims by malicious users. The use of biometrics can provide non-repudiability by ensuring that a purchaser is the bonafide user of the service. However, in applications entailing multiple stakeholders, there may be privacy issues with sharing user's biometrics data. While this concern can be addressed by storing the user's biometric data on the token itself, strong mechanisms are required to ensure that token is both secure and tamper-proof. In this paper, we propose a single use biometric token that relies on Shamir's secret sharing algorithm and blockchain technology to ensure that the encrypted biometric template contained in the token is secure, tamper-proof, and any attempt to use the issued token is irrefutably logged to prove subsequently that the user indeed availed the service. We also analyze issues related to the system security, user privacy, and usability of the proposed solution.

8 citations

Proceedings ArticleDOI
01 Oct 2018
TL;DR: A novel heterogeneity aware loss function within a deep learning framework for periocular biometrics that yields state-of-the-art results in a heterogeneous environment and improves generalizability for cross-database experiments is presented.
Abstract: Mobile biometric approaches provide the convenience of secure authentication with an omnipresent technology. However, this brings an additional challenge of recognizing biometric patterns in an unconstrained environment including variations in mobile camera sensors, illumination conditions, and capture distance. To address the heterogeneous challenge, this research presents a novel heterogeneity aware loss function within a deep learning framework. The effectiveness of the proposed loss function is evaluated for periocular biometrics using the CSIP, IMP and VISOB mobile periocular databases. The results show that the proposed algorithm yields state-of-the-art results in a heterogeneous environment and improves generalizability for cross-database experiments.

8 citations

Proceedings ArticleDOI
12 Dec 2007
TL;DR: This paper describes tradeoff between accuracy of an anonymous system and the security of the biometrics system and proposes, via a k-trial attack model, how the trade-off can be complemented by another security policy such as an account lockout after a given number of attempts.
Abstract: The security, lack of anonymity and revocability of the biometric template are critical issues that need to be addressed in order to vindicate the viability of biometric based authentication systems. Several methods have been proposed to address these problems. However, most of these methods offer lower accuracies than the base system where the template is insecure. This is because in most systems the gain in the security is achieved as a result of loss in non-redundant information. In this paper, we describe tradeoff between accuracy of an anonymous system and the security of the biometrics system. As a case study we start with a highly secure representation of a fingerprint. Then we describe several methods and show experimental results proving that every time we add more information to the secure representation, the accuracy increases, however valuable information is revealed to an adversary. We propose, via a k-trial attack model, how the trade-off can be complemented by another security policy such as an account lockout after a given number of attempts.

8 citations

Proceedings ArticleDOI
01 Sep 2000
TL;DR: A refined approach to learn a class of hierarchical filter banks to extend the enhancement technique and is shown to obtain much better results particularly when the input image is not easily restored using the earlier technique.
Abstract: It is desirable to enhance the fingerprint image for achieving good fingerprint matching performance. In our earlier work, we have proposed a method for learning a set of partitioned least-squares filters from a given set of images and ground truth pairs. In this paper, we propose a refined approach to learn a class of hierarchical filter banks to extend our enhancement technique. The new technique is shown to obtain much better results particularly when the input image is not easily restored using our earlier technique. We evaluate the performance of our new approach to fingerprint enhancement filter design by assessing the effect of enhancement on performance of (i) fingerprint feature extraction and (ii) fingerprint matching.

8 citations


Cited by
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Journal ArticleDOI
TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Abstract: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

14,054 citations

Book
10 Mar 2005
TL;DR: This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators.
Abstract: 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

3,821 citations

Journal ArticleDOI
TL;DR: A fast fingerprint enhancement algorithm is presented, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency.
Abstract: 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.

2,212 citations

01 Apr 1997
TL;DR: The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity.
Abstract: The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

2,188 citations

Reference EntryDOI
15 Oct 2004

2,118 citations