  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.
