Paper Info
Title
A novel encoding scheme for effective biometric discretization: Linearly Separable Subcode.
Abstract
Separability in a code is crucial in guaranteeing a decent Hamming-distance separation among the codewords. In multibit biometric discretization where a code is used for quantization-intervals labeling, separability is necessary for preserving distance dissimilarity when feature components are mapped from a discrete space to a Hamming space. In this paper, we examine separability of Binary Reflected Gray Code (BRGC) encoding and reveal its inadequacy in tackling interclass variation during the discrete-to-binary mapping, leading to a tradeoff between classification performance and entropy of binary output. To overcome this drawback, we put forward two encoding schemes exhibiting full-ideal and near-ideal separability capabilities, known as Linearly Separable Subcode (LSSC) and Partially Linearly Separable Subcode (PLSSC), respectively. These encoding schemes convert the conventional entropy-performance tradeoff into an entropy-redundancy tradeoff in the increase of code length. Extensive experimental results vindicate the superiority of our schemes over the existing encoding schemes in discretization performance. This opens up possibilities of achieving much greater classification performance with high output entropy.
Year
DOI
Venue
2013
10.1109/TPAMI.2012.122
IEEE Trans. Pattern Anal. Mach. Intell.
Keywords
Field
DocType
existing encoding scheme,encoding scheme,conventional entropy-performance tradeoff,effective biometric discretization,linearly separable subcode,near-ideal separability capability,hamming space,greater classification performance,discretization performance,entropy-redundancy tradeoff,classification performance,code length,novel encoding scheme,hamming codes,encoding,indexes,gray codes,feature extraction,quantization,cryptography,hamming distance,entropy,discrete space,labeling
Hamming code,Discretization,Linear separability,Pattern recognition,Computer science,Gray code,Hamming distance,Artificial intelligence,Hamming space,Code (cryptography),Encoding (memory)
Journal
Volume
Issue
ISSN
35
2
1939-3539
Citations 
PageRank 
References 
15
0.66
17
Authors
2
Name
Order
Citations
PageRank
Meng-Hui Lim118822.66
Andrew Beng Jin Teoh21778125.80