TY - JOUR
T1 - A novel encoding scheme for effective biometric discretization
T2 - Linearly separable subcode
AU - Lim, Meng Hui
AU - Teoh, Andrew Beng Jin
N1 - This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the
Korea government (MEST) (No. 2011-8-1095).
PY - 2013/2
Y1 - 2013/2
N2 - 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.
AB - 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.
KW - Biometric discretization
KW - encoding
KW - linearly separable subcode
KW - quantization
UR - http://www.scopus.com/inward/record.url?scp=84871740805&partnerID=8YFLogxK
U2 - 10.1109/TPAMI.2012.122
DO - 10.1109/TPAMI.2012.122
M3 - Journal article
C2 - 22641702
AN - SCOPUS:84871740805
SN - 0162-8828
VL - 35
SP - 300
EP - 313
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
IS - 2
M1 - 6205762
ER -