A Scattering Wavelet Network-based Approach to Fingerprint Classification

Main Article Content

Parmeshwar Birajadar
Vikram Gadre

Abstract

In a large-scale automatic fingerprint identification system (AFIS), fingerprint classification is an essential indexing step to reduce the search time in a large database for accurate matching. Fingerprint classification is still a challenging machine learning problem due to large intra-class and small inter-class variability. Nonlinear elastic deformation is one of the main sources of intra-class variability, which occurs due to the non-uniform pressure applied during fingerprint acquisition and the elastic nature of the fingerprint itself. This paper proposes a novel approach to fingerprint classification based on a scattering wavelet network to extract translation and small deformation invariant local features. The resulting sparse invariant feature vectors are used as input to a simple generative PCA affine classifier for the classification. The experiments evaluated with two different protocols on a benchmark NIST SD-4 database show the effectiveness and robustness of the proposed fingerprint classification algorithm in terms of classification accuracy.

Downloads

Download data is not yet available.

Article Details

How to Cite
1.
Birajadar P, Gadre V. A Scattering Wavelet Network-based Approach to Fingerprint Classification. sms [Internet]. 30Jun.2022 [cited 8Aug.2022];14(02):130-8. Available from: http://smsjournals.com/index.php/SAMRIDDHI/article/view/2716
Section
Research Articles