Password Authentication Using Context-Sensitive Associative Memory Neural Networks: A Novel Approach

Prasad, P. E. S. N. Krishna; Prasad, Bhanu; Chakravarthy, A. S. N.; Avadhani, P. S.

doi:10.1007/978-3-642-27308-7_49

Cited by 3 publications

(12 citation statements)

References 20 publications

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“…Once the keys are extracted from the fusion and face data with various sizes, the generated keys can supplied to the connectionist model or ANN model which chosen here is Context-sensitive Exponent Associative Memory model (CSEAM) [1,2] for encoding and verification process. In this model, memory is acted as exponential kronecker product (eKP) [16,19], which is powerful concept in the field of advanced matrices.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Context-Sensitive Exponent Associative Memory model (CSEAM) [1,2,14] is a novel model for information access, which is general, scalable, operates in parallel with the reasoning, controls the cost of the retrieval and exploits contextual information that improves the performance. A CSEAM is built upon an associative based retrieval manager that can be implemented with vector logic, a context-sensitive search process and a content addressable store.…”

Section: Context-sensitive Exponent Associative Memory Model (Cseam)mentioning

confidence: 99%

“…The connectionist models or Artificial Neural Networks (ANN) [1,16], due to the resemblance its processing has with the form of processing of the human nervous system. They are essential parts of an emerging field of knowledge known as Computational Intelligence.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we describe a connectionist approach often called CSEAM [1,3] model to authenticate the multimodal biometrics , in which the first step is to obtain the keys through PCA followed by SVD decomposition and then apply the CSEAM model using exponential kronecker product [2,17] to encode the key patterns. In the second step the verification process can be done using the same model.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fusion Based Multimodal Authentication in Biometrics Using Context-Sensitive Exponent Associative Memory Model : A Novel Approach

Prasad¹,

K²,

Ramakrishna

et al. 2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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show abstract

Section: Feature Extractionmentioning

confidence: 99%

Section: Context-sensitive Exponent Associative Memory Model (Cseam)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fusion Based Multimodal Authentication in Biometrics Using Context-Sensitive Exponent Associative Memory Model : A Novel Approach

Prasad¹,

K²,

Ramakrishna

et al. 2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…Context-sensitive associative memories are models that allow the retrieval of different vectorial responses given a same vectorial stimulus, depending on the context presented to the memory [6]. The contextualization is obtained by doing the Kronecker product between two vectorial entries to the associative memory: the key stimulus and the context.…”

Section: Context-sensitive Associative Memory Model (Csam)mentioning

confidence: 99%

Performance Evaluation of Password Authentication using Associative Neural Memory Models

Prasasd¹

2012

IJAIT

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Feature Extraction using Sparse SVD for Biometric Fusion in Multimodal Authentication

K¹,

Prasad²,

Ramakrishna³

et al. 2013

IJNSA

Self Cite

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Token based security (ID Cards) have been used to restrictaccess to the Securedsystems.The purpose ofBiometricsistoidentify / verifythe correctness of an individualby using certain physiological orbehaviouraltraits associated with the person.Current biometric systems make use of face, fingerprints,iris,hand geometry,retina, signature, palm print, voiceprint and so on to establish a person’s identity.Biometrics isone of the primary key concepts of realapplicationdomains such asaadhar card, passport,pancard, etc.In this paper, we consider face andfingerprint patternsforidentification/verification.Usingthis data weproposed a novel model for authentication in multimodal biometricsoften called Context-SensitiveExponentAssociative Memory Model (CSEAM).It provides different stagesof securityforbiometricsfusionpatterns.Instage1,fusion offace and finger patternsusingPrincipal ComponentAnalysis (PCA),in stage 2by applyingSparseSVD decomposition toextract the feature patternsfrom thefusion data and face pattern and thenin stage 3,using CSEAM model,theextracted feature vectorscan beencoded.Thefinal key will be stored in the smart cardsas Associative Memory (M), which is often calledContext-Sensitive Associative Memory (CSAM). In CSEAM model,theCSEAMwill be computed usingexponential kronecker productforencodingand verificationofthe chosen samplesfrom the users.Theexponentialof matrixcan be computed in various ways such as Taylor Series, Pade Approximation andalso using OrdinaryDifferential Equations (O.D.E.). Among these approaches we considered first twomethods for computing exponential of a feature space.The result analysis of SVD and Sparse SVD forfeature extraction process and also authentication/verification process of the proposed systemin terms ofperformance measuresasMean square error rateswill be presente

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Password Authentication Using Context-Sensitive Associative Memory Neural Networks: A Novel Approach

Cited by 3 publications

References 20 publications

Fusion Based Multimodal Authentication in Biometrics Using Context-Sensitive Exponent Associative Memory Model : A Novel Approach

Fusion Based Multimodal Authentication in Biometrics Using Context-Sensitive Exponent Associative Memory Model : A Novel Approach

Performance Evaluation of Password Authentication using Associative Neural Memory Models

Feature Extraction using Sparse SVD for Biometric Fusion in Multimodal Authentication

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