Reduced Optimal Feature Based Biometric Authentication Using MALO-MKSVM Techniques

Biometric authentication is referred to as a realistic authentication which traits used is distinct, and quantifiable to recognize one individual. Depending on the level of security required, unimodal based authentication mechanisms are prone to numerous security attacks. In this paper, we propose a...

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Bibliographic Details
Published in:Multimedia tools and applications 2019-11, Vol.78 (22), p.31077-31100
Main Authors: Evangelin, L. Nisha, Fred, A. Lenin
Format: Article
Language:English
Subjects:
Biometrics
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Feature extraction
Image classification
Multimedia Information Systems
Object recognition
Optimization
Principal components analysis
Security
Special Purpose and Application-Based Systems
Support vector machines
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Summary:Biometric authentication is referred to as a realistic authentication which traits used is distinct, and quantifiable to recognize one individual. Depending on the level of security required, unimodal based authentication mechanisms are prone to numerous security attacks. In this paper, we propose a multimodal based biometric recognition framework which will improve the security level by using more than one type of biometric scanner. A new multimodal feature extraction technique has been proposed to reduce the features by utilizing Probabilistic Principal Component Analysis (PPCA) model by the way of choosing optimal features with the assistance of Modified Ant Lion Optimization (MALO). Finally, the recognized and non-recognized images are accomplished by the formation of a new classification model i.e. Multi Kernel Support Vector Machine (MKSVM). From this procedure, the result showed that a high recognition rate and also the most extreme accuracy accomplished in this work.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-019-07918-1