462 research outputs found
Biometric recognition using entropy-based discretization
Author name used in this publication: Kumar, AjayVersion of RecordPublishe
Hand-geometry recognition using entropy-based discretization
Author name used in this publication: Ajay Kumar2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
Improved fuzzy hashing technique for biometric template protection
Biometrics provides a new dimension of security to modern automated applications since each user will need to prove his identity when attempting an access. However, if a stored biometric template is compromised, then the conventional biometric recognition system becomes vulnerable to privacy invasion. This invasion is a permanent one because the biometric template is not replaceable. In this paper, we introduce an improved FuzzyHashing technique for biometric template protection purpose. We demonstrate our implementation in the context of fingerprint biometrics. The experimental results and the security analysis on FVC 2004 DB1 and DB2 fingerprint datasets suggest that the technique is highly feasible in practice
Classification and Verification of Online Handwritten Signatures with Time Causal Information Theory Quantifiers
We present a new approach for online handwritten signature classification and
verification based on descriptors stemming from Information Theory. The
proposal uses the Shannon Entropy, the Statistical Complexity, and the Fisher
Information evaluated over the Bandt and Pompe symbolization of the horizontal
and vertical coordinates of signatures. These six features are easy and fast to
compute, and they are the input to an One-Class Support Vector Machine
classifier. The results produced surpass state-of-the-art techniques that
employ higher-dimensional feature spaces which often require specialized
software and hardware. We assess the consistency of our proposal with respect
to the size of the training sample, and we also use it to classify the
signatures into meaningful groups.Comment: Submitted to PLOS On
DISCRETIZATION OF INTEGRATED MOMENT INVARIANTS FOR WRITER IDENTIFICATION
Conservative regular moments have been proven to
exhibit some shortcomings in the original formulations of
moment functions in terms of scaling factor. Hence, an
incorporated scaling factor of geometric functions into
United Moment Invariant function is proposed for mining
the feature of unconstrained words. Subsequently, the
discrete proposed features undertake discretization
procedure prior to classification for better feature
representation and splendid classification accuracy.
Collectively, discrete values are finite intervals in a
continuous spectrum of values and well known to play
important roles in data mining and knowledge discovery.
Many induction algorithms found in the literature requires
that training data contains only discrete features and some
works better on discretized data; in particular rule based
approaches like rough sets. Hence, in this study, an
integrated scaling formulation of Aspect Scaling Invariant
is presented in Writer Identification to hunt for the
individuality perseverance. Successive exploration is
executed to investigate for the suitability of discretization techniques in probing the issues of writer authorship. Mathematical proving and results of computer
simulations are embraced to attest the feasibility of the
proposed technique in Writer Identification. The results
disclose that the proposed discretized invariants reveal
99% accuracy of classification by using 3520 training
data and 880 testing data
Privacy-Preserving Population-Enhanced Biometric Key Generation from Free-Text Keystroke Dynamics
Biometric key generation techniques are used to reliably generate cryptographic material from biometric signals. Existing constructions require users to perform a particular activity (e.g., type or say a password, or provide a handwritten signature), and are therefore not suitable for generating keys continuously. In this paper we present a new technique for biometric key generation from free-text keystroke dynamics. This is the first technique suitable for continuous key generation. Our approach is based on a scaled parity code for key generation (and subsequent key reconstruction), and can be augmented with the use of population data to improve security and reduce key reconstruction error. In particular, we rely on linear discriminant analysis (LDA) to obtain a better representation of discriminable biometric signals. To update the LDA matrix without disclosing user's biometric information, we design a provably secure privacy-preserving protocol (PP-LDA) based on homomorphic encryption. Our biometric key generation with PP-LDA was evaluated on a dataset of 486 users. We report equal error rate around 5% when using LDA, and below 7% without LDA
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