Texture Classification based on DLBP

Abstract

The uniqueness of a fingerprint can be determined by the pattern of ridges and
furrows as well as the minutiae points. Minutia consists of short ridges, Ridge ending and
Bifurcation. Fingerprint representations are based on the entire image, finger ridges, pores
on the ridges, or salient features derived from the ridges. Representations predominantly
based on ridge endings or bifurcations collectively known as minutiae. The paper proposes
a novel approach to extract image features for Fingerprint texture classification. It makes
use of the features extracted using Dominant local binary patterns (DLBP). The dominant
local binary pattern method makes use of the most frequently occurred patterns to capture
descriptive textural information, while the Gabor-based features aim at supplying
additional global textural information to the DLBP features. These features are classified
using Support Vector Machine (SVM) classifier. It is experimentally demonstrated that the
proposed method achieves the highest classification accuracy in various texture databases
and image conditions