34,088 research outputs found
Study of Fingerprint Enhancement and Matching
Fingerprint is the oldest and popular form of bio-metric identification. Extract Minutiae is most used method for automatic fingerprint matching, every person fingerprint has some unique characteristics called minutiae. But studying the extract minutiae from the fingerprint images and matching it with database is depend on the image quality of finger impression. To make sure the performance of finger impression identification we have to robust the quality of fingerprint image by a suitable fingerprint enhancement algorithm. Here we work with a quick finger impression enhancement algorithm that improve the lucidity of valley and ridge structure based on estimated local orientation and frequency. After enhancement of sample fingerprint, sample fingerprint is matched with the database fingerprints, for that we had done feature extraction, minutiae representation and registration. But due to Spurious and missing minutiae the accuracy of fingerprint matching affected. We had done a detail relevant finger impression matching method build on the Shape Context descriptor, where the hybrid shape and orientation descriptor solve the problem. Hybrid shape descriptor filter out the unnatural minutia paring and ridge orientation descriptor improve the matching score. Matching score is generated and utilized for measuring the accuracy of execution of the proposed algorithm. Results demonstrated that the algorithm is exceptionally satisfactory for recognizing fingerprints acquired from diverse sources. Experimental results demonstrate enhancement algorithm also improves the matching accuracy
Curved Gabor Filters for Fingerprint Image Enhancement
Gabor filters play an important role in many application areas for the
enhancement of various types of images and the extraction of Gabor features.
For the purpose of enhancing curved structures in noisy images, we introduce
curved Gabor filters which locally adapt their shape to the direction of flow.
These curved Gabor filters enable the choice of filter parameters which
increase the smoothing power without creating artifacts in the enhanced image.
In this paper, curved Gabor filters are applied to the curved ridge and valley
structure of low-quality fingerprint images. First, we combine two orientation
field estimation methods in order to obtain a more robust estimation for very
noisy images. Next, curved regions are constructed by following the respective
local orientation and they are used for estimating the local ridge frequency.
Lastly, curved Gabor filters are defined based on curved regions and they are
applied for the enhancement of low-quality fingerprint images. Experimental
results on the FVC2004 databases show improvements of this approach in
comparison to state-of-the-art enhancement methods
Accelerated Fingerprint Enhancement: A GPU-Optimized Mixed Architecture Approach
This document presents a preliminary approach to latent fingerprint
enhancement, fundamentally designed around a mixed Unet architecture. It
combines the capabilities of the Resnet-101 network and Unet encoder, aiming to
form a potentially powerful composite. This combination, enhanced with
attention mechanisms and forward skip connections, is intended to optimize the
enhancement of ridge and minutiae features in fingerprints. One innovative
element of this approach includes a novel Fingerprint Enhancement Gabor layer,
specifically designed for GPU computations. This illustrates how modern
computational resources might be harnessed to expedite enhancement. Given its
potential functionality as either a CNN or Transformer layer, this Gabor layer
could offer improved agility and processing speed to the system. However, it is
important to note that this approach is still in the early stages of
development and has not yet been fully validated through rigorous experiments.
As such, it may require additional time and testing to establish its robustness
and usability in the field of latent fingerprint enhancement. This includes
improvements in processing speed, enhancement adaptability with distinct latent
fingerprint types, and full validation in experimental approaches such as
open-set (identification 1:N) and open-set validation, fingerprint quality
evaluation, among others
FIGO: Enhanced Fingerprint Identification Approach Using GAN and One Shot Learning Techniques
Fingerprint evidence plays an important role in a criminal investigation for
the identification of individuals. Although various techniques have been
proposed for fingerprint classification and feature extraction, automated
fingerprint identification of fingerprints is still in its earliest stage. The
performance of traditional \textit{Automatic Fingerprint Identification System}
(AFIS) depends on the presence of valid minutiae points and still requires
human expert assistance in feature extraction and identification stages. Based
on this motivation, we propose a Fingerprint Identification approach based on
Generative adversarial network and One-shot learning techniques (FIGO). Our
solution contains two components: fingerprint enhancement tier and fingerprint
identification tier. First, we propose a Pix2Pix model to transform low-quality
fingerprint images to a higher level of fingerprint images pixel by pixel
directly in the fingerprint enhancement tier. With the proposed enhancement
algorithm, the fingerprint identification model's performance is significantly
improved. Furthermore, we develop another existing solution based on Gabor
filters as a benchmark to compare with the proposed model by observing the
fingerprint device's recognition accuracy. Experimental results show that our
proposed Pix2pix model has better support than the baseline approach for
fingerprint identification. Second, we construct a fully automated fingerprint
feature extraction model using a one-shot learning approach to differentiate
each fingerprint from the others in the fingerprint identification process. Two
twin convolutional neural networks (CNNs) with shared weights and parameters
are used to obtain the feature vectors in this process. Using the proposed
method, we demonstrate that it is possible to learn necessary information from
only one training sample with high accuracy
- …