Perfect fingerprint orientation fields by locally adaptive global models

Abstract

Fingerprint recognition is widely used for verification and identification in many commercial, governmental and forensic applications. The orientation field (OF) plays an important role at various processing stages in fingerprint recognition systems. OFs are used for image enhancement, fingerprint alignment, for fingerprint liveness detection, fingerprint alteration detection and fingerprint matching. In this paper, a novel approach is presented to globally model an OF combined with locally adaptive methods. We show that this model adapts perfectly to the 'true OF' in the limit. This perfect OF is described by a small number of parameters with straightforward geometric interpretation. Applications are manifold: Quick expert marking of very poor quality (for instance latent) OFs, high fidelity low parameter OF compression and a direct road to ground truth OFs markings for large databases, say. In this contribution we describe an algorithm to perfectly estimate OF parameters automatically or semi-automatically, depending on image quality, and we establish the main underlying claim of high fidelity low parameter OF compression.

Möbius Moduli for Fingerprint Orientation Fields

“…In conclusion we remark that new algorithms for generating realistic synthetic orientation fields have to be developed. The recently proposed XQD model Gottschlich et al (2017) could be used for this purpose, realistically linking curvature with conformality indeces. Until their arrival, one may rely on orientation fields from real fingerprints (as implemented by RFC Imdahl et al (2015)).…”

Alignment-Free Cross-Sensor Fingerprint Matching Based on the Co-Occurrence of Ridge Orientations and Gabor-HoG Descriptor

“…Ridge orientation field is computed using the technique introduced in [12], where the dominant ridge orientation field is calculated by combining the gradient estimates within a window of size w × w centered at location (i, j): where and are the gradient magnitudes in the and directions, respectively, is in the range [0, ]. We applied Sobel operator to compute and because it has been extensively used to compute the gradients of fingerprints [13]- [15]. The Gaussian filter is applied to smooth the orientation of a window and to suppress noise as follows:…”

“…A fingerprint contains connected ridges. The distance between ridges is an important visual cue for fingerprint recognition [1,34], but it offers difficulty when dealing with fingerprint sensor interoperability [12][13][14][15]. Figure 5 exhibits four fingerprints and their thinned versions.…”

Detecting Anisotropy in Fingerprint Growth

“…() and Gottschlich et al . () such that the nearly parallel friction ridges above the crease are oriented vertically, so that the positive horizontal axis points into the distal direction; Fig. .…”

“…Semiautomated alignment (FVC2002 DB2 database finger 7, print 4) of crease lines along the vertical axis, using the (extended) quadratic differential tool fromHuckemann et al (2008) andGottschlich et al (2017), such that the horizontal axis coincides with the distal axis (2017) such that the nearly parallel friction ridges above the crease are oriented vertically, so that the positive horizontal axis points into the distal direction;Fig. 3.…”