Fingerprint Image Enhancement:Segmentation to Thinning

Abstract

Fingerprint has remained a very vital index for human recognition. In the field of security, series of Automatic Fingerprint Identification Systems (AFIS) have been developed. One of the indices for evaluating the contributions of these systems to the enforcement of security is the degree with which they appropriately verify or identify input fingerprints. This degree is generally determined by the quality of the fingerprint images and the efficiency of the algorithm. In this paper, some of the sub-models of an existing mathematical algorithm for the fingerprint image enhancement were modified to obtain new and improved versions. The new versions consist of different mathematical models for fingerprint image segmentation, normalization, ridge orientation estimation, ridge frequency estimation, Gabor filtering, binarization and thinning. The implementation was carried out in an environment characterized by Window Vista Home Basic operating system as platform and Matrix Laboratory (MatLab) as frontend engine. Synthetic images as well as real fingerprints obtained from the FVC2004 fingerprint database DB3 set A were used to test the adequacy of the modified sub-models and the resulting algorithm. The results show that the modified sub-models perform well with significant improvement over the original versions. The results also show the necessity of each level of the enhancement.

The Mathematical Model and Deep Learning Features Selection for Whorl Fingerprint Classifications

“…Thinning is a basic method that constructs a skeleton for the input fingerprint image because it is a technique that takes a fingerprint binary image and renders the ridges that exist in the print just one pixel wide without altering the overall pattern and leaving holes in the ridges to create a kind of image skeleton. This method helps us to find the tangential direction of the ridges at a point (x,y), where 0 ≤ (x, y) < π and omits a the redundant data so that the thinning preprocessing step is used before simulation [28].…”

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“…Segmentation and orientation estimation are both part of fingerprint image enhancement. Each enhancement study aims to improve the ridge structures’ image quality in the recoverable parts while weakening or eliminating the unrecoverable regions Figure a depicts a gray conversion of original fingerprint images with background and foreground regions.…”

“…Each enhancement study aims to improve the ridge structures' image quality in the recoverable parts while weakening or eliminating the unrecoverable regions. 62 Figure 8a depicts a gray conversion of original fingerprint images with background and foreground regions. Segmentation is a preprocessing technique necessary to prevent the extraction of details from background noise.…”

“…The image is divided into blocks of 12 × 12 pixels as the first phase in the frequency estimate process. 62 The gray-level values of all of the pixels inside each block are then projected in a direction orthogonal to the orientation of the nearby ridge. The ridges in the fingerprint are represented by the local minimum points of this projection, which create a wave with an almost sinusoidal pattern.…”

“…In addition to the orientation image, the local ridge frequency is an important component of creating the Gabor filter. The image is divided into blocks of 12 × 12 pixels as the first phase in the frequency estimate process . The gray-level values of all of the pixels inside each block are then projected in a direction orthogonal to the orientation of the nearby ridge.…”