Feature Selection in Multimodal Authentication Using Lu Factorization with Cseam Model
Y. Suresh, Pavan Kumar K, Krishna Prasad PESN, Prasad BDCN · International Journal on Computational Science & Applications · 2015
Multimodal authentication is one of the prime concepts in current applications of real scenario. Various approaches have been proposed in this aspect. In this paper, an intuitive strategy is proposed as a framework for providing more secure key in biometric security aspect. Initially the features will be extracted through PCA by SVD from the chosen biometric patterns, then using LU factorization technique key components will be extracted, then selected with different key sizes and then combined the selected key components using convolution kernel method (Exponential Kronecker Product - eKP) as Context-Sensitive Exponent Associative Memory model (CSEAM). In the similar way, the verification process will be done and then verified with the measure MSE. This model would give better outcome when compared with SVD factorization[1] as feature selection. The process will be computed for different key sizes and the results will be presented.