A New Path to Construct Parametric Orientation Field: Sparse FOMFE Model and Compressed Sparse FOMFE Model

TL;DR

This paper introduces two innovative parametric models, sparse FOMFE and compressed sparse FOMFE, for constructing fingerprint orientation fields efficiently using sparse representation and compressed sensing, suitable for large-scale fingerprint indexing.

Contribution

The paper proposes two novel parametric models based on sparse representation and compressed sensing for fingerprint orientation field construction, improving efficiency and scalability.

Findings

Models effectively construct orientation fields from various fingerprint qualities.

Sparse and compressed models outperform traditional methods in speed and data compression.

Potential for large-scale fingerprint indexing applications.

Abstract

Orientation field, representing the fingerprint ridge structure direction, plays a crucial role in fingerprint-related image processing tasks. Orientation field is able to be constructed by either non-parametric or parametric methods. In this paper, the advantages and disadvantages regarding to the existing non-parametric and parametric approaches are briefly summarized. With the further investigation for constructing the orientation field by parametric technique, two new models - sparse FOMFE model and compressed sparse FOMFE model are introduced, based on the rapidly developing signal sparse representation and compressed sensing theories. The experiments on high-quality fingerprint image dataset (plain and rolled print) and poor-quality fingerprint image dataset (latent print) demonstrate their feasibilities to construct the orientation field in a sparse or even compressed sparse mode. The comparisons among the state-of-art orientation field modeling approaches show that the proposed two models have the potential availability in big data-oriented fingerprint indexing tasks.