Multi-modal Authentication Using Continuous Dynamic Programming

Radhika, K. R.; Sheela, S. V.; Venkatesha, M. K.; Sekhar, G. N.

doi:10.1007/978-3-642-04391-8_30

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Quad tree further depth decomposition will lead to more performance as it selects more minute details of variation. Continuous dynamic programming method of classification will provide piecewise comparison (Radhika et al, 2009a). Zernike moments are rotation invariant (Radhika et al, 2009b).…”

Section: Resultsmentioning

confidence: 99%

Off-Line Signature Authentication Based on Moment Invariants Using Support Vector Machine

Radhika¹

2010

Journal of Computer Science

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Problem statement: The research addressed the computational load reduction in off-line signature verification based on minimal features using bayes classifier, fast Fourier transform, linear discriminant analysis, principal component analysis and support vector machine approaches. Approach: The variation of signature in genuine cases is studied extensively, to predict the set of quad tree components in a genuine sample for one person with minimum variance criteria. Using training samples, with a high degree of certainty the Minimum Variance Quad tree Components (MVQC) of a signature for a person are listed to apply on imposter sample. First, Hu moment is applied on the selected subsections. The summation values of the subsections are provided as feature to classifiers. Results: Results showed that the SVM classifier yielded the most promising 8% False Rejection Rate (FRR) and 10% False Acceptance Rate (FAR). The signature is a biometric, where variations in a genuine case, is a natural expectation. In the genuine signature, certain parts of signature vary from one instance to another. Conclusion: The proposed system aimed to provide simple, faster robust system using less number of features when compared to state of art works

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Section: Resultsmentioning

confidence: 99%

Off-Line Signature Authentication Based on Moment Invariants Using Support Vector Machine

Radhika¹

2010

Journal of Computer Science

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“…The input parameter for transfer function isb s2 ðtÞ. Pole-zero mapping is achieved by (27). The real part of zeros (28) are considered, for improved comparison.…”

Section: Methodologiesmentioning

confidence: 99%

A Novel Approach for Authentication of Online Handwritten Signature Based on Residuals, Poles and Zeros

Radhika¹,

Venkatesha²,

Sekhar

2011

Int. J. Patt. Recogn. Artif. Intell.

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This paper proposes an online signature authentication based on polynomial modeling. The derived feature acceleration is used. The order of the polynomial is carefully selected for each subject using the acceleration vector. The dynamic warping algorithm and mean squared error measure aid in standard template selection process for a subject. The best¯t order is directly dependent on the cursive information of the signature sample. We provide a practical analysis of residual term, number of zeros inside the unit circle and distance measure on pole values for the purpose of classi¯cation. Storing order and standard template for a subject, reduces the runtime and memory requirements for a dynamic system. The best performance was achieved based on the number of zeros inside unit circle with false rejection rate as 5% and false acceptance rate as 7%. The aim of the work is to provide simple robust authentication suitable for hand held devices. Even compared to state-of art experiments, the proposed system is aimed to provide authentication using a single derived feature.

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“…Algorithms for speeding-up the k-nn search fall into two categories: template condensation and template reorganization. Template condensation removes redundant patterns in a template set and template reorganization, restructures templates for efficient search of k nearest neighbors [19]. In this work, template condensation is achieved by genuine training samples selected.…”

Section: K-nn Methodsmentioning

confidence: 99%

Biometric Authentication Using Nonparametric Methods

Sheela¹,

Radhika²

2010

IJCSIT

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The physiological and behavioral trait is employed to develop biometric authentication systems. The proposed work deals with the authentication of iris and signature based on minimum variance criteria. The iris patterns are preprocessed based on area of the connected components. The segmented image used for authentication consists of the region with large variations in the gray level values. The image region is split into quadtree components. The components with minimum variance are determined from the training samples. Hu moments are applied on the components. The summation of moment values corresponding to minimum variance components are provided as input vector to k-means and fuzzy kmeans classifiers. The best performance was obtained for MMU database consisting of 45 subjects. The number of subjects with zero False Rejection Rate [FRR] was 44 and number of subjects with zero False Acceptance Rate [FAR] was 45. This paper addresses the computational load reduction in off-line signature verification based on minimal features using k-means, fuzzy k-means, k-nn, fuzzy k-nn and novel average-max approaches. FRR of 8.13% and FAR of 10% was achieved using k-nn classifier. The signature is a biometric, where variations in a genuine case, is a natural expectation. In the genuine signature, certain parts of signature vary from one instance to another. The system aims to provide simple, fast and robust system using less number of features when compared to state of art works.

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Multi-modal Authentication Using Continuous Dynamic Programming

Cited by 6 publications

References 10 publications

Off-Line Signature Authentication Based on Moment Invariants Using Support Vector Machine

Off-Line Signature Authentication Based on Moment Invariants Using Support Vector Machine

A Novel Approach for Authentication of Online Handwritten Signature Based on Residuals, Poles and Zeros

Biometric Authentication Using Nonparametric Methods

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