Feature Extraction using Sparse SVD for Biometric Fusion in Multimodal Authentication

Abstract

Token based security (ID Cards) have been used to restrict access to the Secured systems.The purpose of Biometrics is to identify / verify the correctness of an individual by using certain physiological or behavioural traits 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 is one of the primary key concepts of real application domains such as aadhar card, passport, pan card, etc. In this paper, we consider face and fingerprint patterns for identification/verification. Using this data we proposed a novel model for authentication in multimodal biometrics often called Context-Sensitive Exponent Associative Memory Model (CSEAM). It provides different stages of security for biometrics fusion patterns. In stage 1, fusion of face and finger patterns using Principal ComponentAnalysis (PCA), in stage 2 by applying Sparse SVD decomposition to extract the feature patterns from the fusion data and face pattern and then in stage 3, using CSEAM model, the extracted feature vectors can be encoded.The final key will be stored in the smart cards as Associative Memory (M), which is often called Context-Sensitive Associative Memory (CSAM).In CSEAM model, the CSEAM will be computed using exponential kronecker product for encoding and verification of the chosen samples from the users.The exponential of matrix can be computed in various ways such as Taylor Series, Pade Approximation and also using Ordinary Differential Equations (O.D.E.).Among these approaches we considered first two methods for computing exponential of a feature space.The result analysis of SVD and Sparse SVD for feature extraction process and also authentication/verification process of the proposed system in terms of performance measures as Mean square error rates will be presented.