Texture Classification based on DLBP
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Publication date: 2019-11-23
Eurasian J Anal Chem 2017;12(4):224–234
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.