Paper Info
Title
Discriminative and Non-User Specific Binary Biometric Representation via Linearly-Separable SubCode Encoding-based Discretization.
Abstract
Biometric discretization is a process of transforming continuous biometric features of an identity into a binary bit string. This paper mainly focuses on improving the global discretization method - a discretization method that does not base on information specific to each user in bitstring extraction, which appears to be important in applications that prioritize strong security provision and strong privacy protection. In particular, we demonstrate how the actual performance of a global discretization could further be improved by embedding a global discriminative feature selection method and a Linearly Separable Subcode-based encoding technique. In addition, we examine a number of discriminative feature selection measures that can reliably be used for such discretization. Lastly, encouraging empirical results vindicate the feasibility of our approach.
Year
DOI
Venue
2011
10.3837/tiis.2011.02.008
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
Keywords
Field
DocType
Biometric,discretization,quantization,encoding,linearly separable subcode
Linear separability,Discretization,Feature selection,Pattern recognition,Computer science,Artificial intelligence,Biometrics,Discriminative model,Bit array,Discretization of continuous features,Encoding (memory)
Journal
Volume
Issue
ISSN
5
2
1976-7277
Citations 
PageRank 
References 
2
0.37
2
Authors
2
Name
Order
Citations
PageRank
Meng-Hui Lim118822.66
Andrew Beng Jin Teoh21778125.80