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VIrtual Pin Based On Biometrics


Secure networking is a major key for communications in our society, whereas biometrics are the only authentication methods that can verify the identity of a person. However, due to incompatibility with existing infrastructure biometrics are often not introduced. The Virtual Pin links biometrics and cryptographic authentication to solve the problem. Even though the Virtual Pin is calculated from biometrics, it may be chosen freely and changed as often as desired.

The Virtual Pin offers two unique benefits over conventional biometric methods:
1. No templates have to be stored. (Templates might be read out by attackers);
2. The Virtual Pin can be linked to cryptographic infrastructure. It can for example emulate a Pin to the ATM. It can as well be used as a DES and encrypt a Keberos ticket.

The Virtual PIN (later on VP) solves compatibility & privacy issues. An obstacle of biometric methods is that they are incompatible with existing infrastructure. The VP links biometrics with cryptographic authentication. Conventional methods compare a biometric trait with a stored sample (template). The VP maps a user' s biometric trait to a unique number, no template has to be stored. The VP is obtained by mapping a vector from fingerprints onto a unique value using error correcting codes . The feature extraction algorithm follows a suggestion by Jain; other possibilities should be explored. The error correcting codes employed are algebraic codes.

Work description:
The work consists of the following steps:
WPl Image Processing: Preprocess the images read from the fingerprint scanner to generate appropriate input to the feature extraction algorithm, e.g. noise reduction, smoothing, and scaling. Find the support, orientation field, and a reference frame of the image;
WP2 Design masks and filters used to extract local features. Examine the suitability of global fingerprint properties;
WP3 Functional test frame: Supply a base for system and subsystem evaluation tests. Collect the images needed for testing. Measure performance of subsystems. Determine false acceptance rate and false rejection rates;
WP4 Statistical, Analysis of Feature Vectors: Assure that the representation of the parameter values harvest maximum amount of information. Analysis of the statistical properties of the errors of the feature vectors. Examination of the clustering of the feature vectors of an individual in the feature space;
WP5 Error Correcting Codes : Construct suitable error correcting codes Compare linear and non - linear codes with short block length. Iterative decoding;
WP6 Cryptography: Define protocol that prescribes how to deal with: The fingerprints; The correction information; The feature vector; The secret keys which are to protected biometrically, and maybe additional objects;
WP7 Application Concept: An actual implementation should be sketched discussed with prospective sponsors;
WP8 System Evaluation and Optimisation. Determine overall system performance with respect to performance and security needs of targeted applications using the test framework of WPJ;
WP9 Application Prototype: A prototype, demonstrating the feasibility of the method, should be developed;
WP10 Project Management: Insure that work packages are completed in time and quality requirements are met.

M1: Implementation of reference points within defined margin of error;
M2: Version 2.0 of the feature extraction software;
M3: Testsuite covering all submodules;
M4 : Thorough understanding of channel properties;
M5: Enhanced version of the error correcting module;
M6: Document assessing the security;
M7: Concept of introduction within existing application;
M8: Reliable system concept;
M9: Application prototype;
M10: Timely reports to EU.

Funding Scheme

CSC - Cost-sharing contracts


Prinzregentenstrasse 159
800729 Muenchen

Participants (2)

46, Rue Barrault
75634 Paris 13
32 Haim Levanon Street
61392 Tel Aviv Yafo