TITLE:
Local Binary Patterns and Its Variants for Finger Knuckle Print Recognition in Multi-Resolution Domain
AUTHORS:
D. R. Arun, C. Christopher Columbus, K. Meena
KEYWORDS:
Biometrics, Finger Knuckle Print, Contourlet Transform, Local Binary Pattern (LBP), Local Directional Pattern (LDP), Local Derivative Ternary Pattern (LDTP), Local Texture Description Framework Based Modified Local Directional Pattern (LTDF_MLDN), Nearest Neighbor (NN) Classifier, Extreme Learning Machine (ELM) Classifier
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.10,
August
22,
2016
ABSTRACT: Finger Knuckle Print
biometric plays a vital role in
establishing security for real-time environments. The success of human
authentication depends on high speed and accuracy. This paper proposed an integrated approach of
personal authentication using texture based Finger Knuckle Print (FKP) recognition in
multiresolution domain. FKP
images are rich in texture patterns. Recently, many texture patterns are
proposed for biometric feature extraction. Hence, it is essential to review
whether Local Binary Patterns or its variants perform well for FKP recognition.
In this paper, Local Directional
Pattern (LDP), Local Derivative Ternary Pattern (LDTP) and Local Texture Description
Framework based Modified Local Directional Pattern
(LTDF_MLDN) based feature extraction in multiresolution domain are experimented
with Nearest Neighbor and Extreme Learning Machine (ELM) Classifier for FKP
recognition. Experiments were
conducted on PolYU database. The
result shows that LDTP in Contourlet domain achieves a promising performance.
It also proves that Soft classifier performs better than the hard classifier.