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  • 标题:Fusion Based Multimodal Authentication in Biometrics Using Context-Sensitive Exponent Associative Memory Model : A Novel Approach
  • 本地全文:下载
  • 作者:P. E. S. N. Krishna Prasad ; Pavan Kumar K ; M. V. Ramakrishna
  • 期刊名称:Computer Science & Information Technology
  • 电子版ISSN:2231-5403
  • 出版年度:2013
  • 卷号:3
  • 期号:6
  • 页码:81-90
  • DOI:10.5121/csit.2013.3608
  • 出版社:Academy & Industry Research Collaboration Center (AIRCC)
  • 摘要:Biometrics is one of the primary key concepts of re al application domains such as aadhar card, passport, pan card, etc. In such applications user can provide two to three biometrics patterns like face, finger, palm, signature, iris data, and so on. We considered face and finger patterns for encoding and then also for 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 patterns. In stage 1, face and finger patterns can be fusion through Principal Component Analysis (PCA), in stage 2 by applying SVD decomposition to generate keys from the fusion data and pre-processed face pattern and then in stage 3, using CSEAM model the generated keys can be encoded. The final key will be stored in the smart cards. In CSEAM model, exponential kronecker product plays a critical role for encoding and also for verification to verify the chosen samples from the users. This paper discusses by considering realistic biometric data in terms of time and space
  • 关键词:Biometrics; Biometric fusion; Face; Finger; Context-Sensitive Exponent Associative Memory ;Model (CSEAM); Kronecker Product; Exponential Kronecker Product (eKP); Multimodal ;Authentication
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