Pattern Recognition Using Context Dependent Memory Model (CDMM) in Multimodal Authentication System

Suresh, Y.; Kumar, K Pavan; Prasad, Pesn. Krishna; BDCN, Prasad

doi:10.5121/ijfcst.2015.5105

Cited by 2 publications

(4 citation statements)

References 25 publications

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“…The Mean Square Error is equal to the sum of the variance and the squared bias of the estimator or of the predictions. In the case of the MSE of an estimator [9,14],…”

Section: Decision Strategymentioning

confidence: 99%

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Multimodal Authentication using Tensor Algebra

2020

IJETER

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Tensors play a major role in the process of image patterns by considering the concept of approximations by varying orders of polynomials. Tensors define a normal decomposition of uniform patterns that leads the connection between the symmetric tensors and multivariate polynomials. One of the inductive learning concepts is feature selection. The selection of a subset of features from the given list of features is by considering an evaluation measure i.e., the subset of features with specified size, the smaller size subset features satisfies with certain restriction on the chosen measure and the set with best features among its size and the value of its measure. The objective of this is to improve the inductive learning process. Feature extraction and selection is one key concept for the selection of best features in order to analyze and represent the patterns through the concept of Tensor objects. Numerous traditional approaches exist for the extraction of features in the process of image patterns such as PCA, Sparse PCA, Kernel PCA, SVD, and Sparse SVD. Among these approaches, to analyze the tensor features for the classification and recognition tensor based matrix factorization techniques for the extraction and selection of features. In this, by considering biometrics features such as face and finger of a person in multimodal authentication system by applying Nonnegative Tucker Factorization (NTF) for feature selection and extraction, Principle Component Analysis (PCA) for image fusion. Finally, results were proposed.

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“…The Mean Square Error is equal to the sum of the variance and the squared bias of the estimator or of the predictions. In the case of the MSE of an estimator [9,14],…”

Section: Decision Strategymentioning

confidence: 99%

“…The data elements can be represented as monadic, dyadic and so on. In monadic, the data is represented as one dimensional, where as in dyadic representation data is stored in the form of rows and columns [9].…”

Section: Introductionmentioning

confidence: 99%

Multimodal Authentication using Tensor Algebra

2020

IJETER

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Section: Vector Logicmentioning

confidence: 99%

“…In the present research the convolution kernel products [16,17] such as Kronecker Product and application of it, Khatri Rao are used in the areas of image processing for exactness and effectiveness.…”

Section: A Convolution Kernel Productsmentioning

confidence: 99%

Feature Extraction and Analysis in Multimodal Biometric Authentication using Lu Factorization with Kronecker Algebra

Suresh¹

2019

IJITEE

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Pattern recognition is one of the current and advanced technologies that focus on analysis and construction of pattern is a complex work. For recognition of patterns Vector logic gives good strategies. This paper focuses on pattern recognition in multimodal authentication system by using vector logic. A framework has been proposed to provide more security in biometric aspect. Initially, features are extracted through PCA from the normalized biometric imaginaries, and then using LU factorization key components are extracted. By using convolution kernel methods such as Khatri Rao an application of Kronecker product weights are computed for different key sizes. In the same way verification process is implemented and verified with MSE. This framework gives better result for chosen threshold value.

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Pattern Recognition Using Context Dependent Memory Model (CDMM) in Multimodal Authentication System

Cited by 2 publications

References 25 publications

Multimodal Authentication using Tensor Algebra

Multimodal Authentication using Tensor Algebra

Feature Extraction and Analysis in Multimodal Biometric Authentication using Lu Factorization with Kronecker Algebra

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