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Showing papers by "Nalini K. Ratha published in 2006"


Proceedings ArticleDOI
20 Aug 2006
TL;DR: This work presents several constructs for cancelable templates using feature domain transformations and empirically examines their efficacy, and presents a method for accurate registration which is a key step in building cancelable transforms.
Abstract: Biometrics offers usability advantages over traditional token and password based authentication schemes, but raises privacy and security concerns. When compromised, credit cards and passwords can be revoked or replaced while biometrics are permanently associated with a user and cannot be replaced. Cancelable biometrics attempt to solve this by constructing revocable biometric templates. We present several constructs for cancelable templates us- ing feature domain transformations and empirically exam- ine their efficacy. We also present a method for accurate registration which is a key step in building cancelable trans- forms. The overall approach has been tested using large databases and our results demonstrate that without losing much accuracy, we can build a large number of cancelable transforms for fingerprints.

185 citations


Proceedings ArticleDOI
17 Jun 2006
TL;DR: Accuracy can be significantly improved at the expense of few extra matches in the fingerprint verification procedure using a publicly available database and matcher and a Support Vector Machine (SVM)-based classifier.
Abstract: Most biometric verification techniques make decisions based solely on a score that represents the similarity of the query template with the reference template of the claimed identity stored in the database. When multiple templates are available, a fusion scheme can be designed using the similarities with these templates. Combining several templates to construct a composite template and selecting a set of useful templates has also been reported in addition to usual multi-classifier fusion methods when multiple matchers are available. These commonly adopted techniques rarely make use of the large number of non-matching templates in the database or training set. In this paper, we highlight the usefulness of such a fusion scheme while focusing on the problem of fingerprint verification. For each enrolled template, we identify its cohorts (similar fingerprints) based on a selection criterion. The similarity scores of the query template with the reference template and its cohorts from the database are used to make the final verification decision using two approaches: a likelihood ratio based normalization scheme and a Support Vector Machine (SVM)-based classifier. We demonstrate the accuracy improvements using the proposed method with no a priori knowledge about the database or the matcher under consideration using a publicly available database and matcher. Using our cohort selection procedure and the trained SVM, we show that accuracy can be significantly improved at the expense of few extra matches.

40 citations