2,679 research outputs found
Real time ridge orientation estimation for fingerprint images
Fingerprint verification is an important bio-metric technique for personal
identification. Most of the automatic verification systems are based on
matching of fingerprint minutiae. Extraction of minutiae is an essential
process which requires estimation of orientation of the lines in an image. Most
of the existing methods involve intense mathematical computations and hence are
performed through software means. In this paper a hardware scheme to perform
real time orientation estimation is presented which is based on pipelined
architecture. Synthesized circuits proved the functionality and accuracy of the
suggested method.Comment: 8 pages, 15 figures, 1 tabl
A New Path to Construct Parametric Orientation Field: Sparse FOMFE Model and Compressed Sparse FOMFE Model
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
Perfect Fingerprint Orientation Fields by Locally Adaptive Global Models
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
Fingerprint Distortion Rectification using Deep Convolutional Neural Networks
Elastic distortion of fingerprints has a negative effect on the performance
of fingerprint recognition systems. This negative effect brings inconvenience
to users in authentication applications. However, in the negative recognition
scenario where users may intentionally distort their fingerprints, this can be
a serious problem since distortion will prevent recognition system from
identifying malicious users. Current methods aimed at addressing this problem
still have limitations. They are often not accurate because they estimate
distortion parameters based on the ridge frequency map and orientation map of
input samples, which are not reliable due to distortion. Secondly, they are not
efficient and requiring significant computation time to rectify samples. In
this paper, we develop a rectification model based on a Deep Convolutional
Neural Network (DCNN) to accurately estimate distortion parameters from the
input image. Using a comprehensive database of synthetic distorted samples, the
DCNN learns to accurately estimate distortion bases ten times faster than the
dictionary search methods used in the previous approaches. Evaluating the
proposed method on public databases of distorted samples shows that it can
significantly improve the matching performance of distorted samples.Comment: Accepted at ICB 201
End-to-End Latent Fingerprint Search
Latent fingerprints are one of the most important and widely used sources of
evidence in law enforcement and forensic agencies. Yet the performance of the
state-of-the-art latent recognition systems is far from satisfactory, and they
often require manual markups to boost the latent search performance. Further,
the COTS systems are proprietary and do not output the true comparison scores
between a latent and reference prints to conduct quantitative evidential
analysis. We present an end-to-end latent fingerprint search system, including
automated region of interest (ROI) cropping, latent image preprocessing,
feature extraction, feature comparison , and outputs a candidate list. Two
separate minutiae extraction models provide complementary minutiae templates.
To compensate for the small number of minutiae in small area and poor quality
latents, a virtual minutiae set is generated to construct a texture template. A
96-dimensional descriptor is extracted for each minutia from its neighborhood.
For computational efficiency, the descriptor length for virtual minutiae is
further reduced to 16 using product quantization. Our end-to-end system is
evaluated on three latent databases: NIST SD27 (258 latents); MSP (1,200
latents), WVU (449 latents) and N2N (10,000 latents) against a background set
of 100K rolled prints, which includes the true rolled mates of the latents with
rank-1 retrieval rates of 65.7%, 69.4%, 65.5%, and 7.6% respectively. A
multi-core solution implemented on 24 cores obtains 1ms per latent to rolled
comparison
Latent Fingerprint Registration via Matching Densely Sampled Points
Latent fingerprint matching is a very important but unsolved problem. As a
key step of fingerprint matching, fingerprint registration has a great impact
on the recognition performance. Existing latent fingerprint registration
approaches are mainly based on establishing correspondences between minutiae,
and hence will certainly fail when there are no sufficient number of extracted
minutiae due to small fingerprint area or poor image quality. Minutiae
extraction has become the bottleneck of latent fingerprint registration. In
this paper, we propose a non-minutia latent fingerprint registration method
which estimates the spatial transformation between a pair of fingerprints
through a dense fingerprint patch alignment and matching procedure. Given a
pair of fingerprints to match, we bypass the minutiae extraction step and take
uniformly sampled points as key points. Then the proposed patch alignment and
matching algorithm compares all pairs of sampling points and produces their
similarities along with alignment parameters. Finally, a set of consistent
correspondences are found by spectral clustering. Extensive experiments on
NIST27 database and MOLF database show that the proposed method achieves the
state-of-the-art registration performance, especially under challenging
conditions
Generative Convolutional Networks for Latent Fingerprint Reconstruction
Performance of fingerprint recognition depends heavily on the extraction of
minutiae points. Enhancement of the fingerprint ridge pattern is thus an
essential pre-processing step that noticeably reduces false positive and
negative detection rates. A particularly challenging setting is when the
fingerprint images are corrupted or partially missing. In this work, we apply
generative convolutional networks to denoise visible minutiae and predict the
missing parts of the ridge pattern. The proposed enhancement approach is tested
as a pre-processing step in combination with several standard feature
extraction methods such as MINDTCT, followed by biometric comparison using MCC
and BOZORTH3. We evaluate our method on several publicly available latent
fingerprint datasets captured using different sensors
Fingerprint liveness detection using local quality features
Fingerprint-based recognition has been widely deployed in various
applications. However, current recognition systems are vulnerable to spoofing
attacks which make use of an artificial replica of a fingerprint to deceive the
sensors. In such scenarios, fingerprint liveness detection ensures the actual
presence of a real legitimate fingerprint in contrast to a fake
self-manufactured synthetic sample. In this paper, we propose a static
software-based approach using quality features to detect the liveness in a
fingerprint. We have extracted features from a single fingerprint image to
overcome the issues faced in dynamic software-based approaches which require
longer computational time and user cooperation. The proposed system extracts 8
sensor independent quality features on a local level containing minute details
of the ridge-valley structure of real and fake fingerprints. These local
quality features constitutes a 13-dimensional feature vector. The system is
tested on a publically available dataset of LivDet 2009 competition. The
experimental results exhibit supremacy of the proposed method over current
state-of-the-art approaches providing least average classification error of
5.3% for LivDet 2009. Additionally, effectiveness of the best performing
features over LivDet 2009 is evaluated on the latest LivDet 2015 dataset which
contain fingerprints fabricated using unknown spoof materials. An average
classification error rate of 4.22% is achieved in comparison with 4.49%
obtained by the LivDet 2015 winner. Further, the proposed system utilizes a
single fingerprint image, which results in faster implications and makes it
more user-friendly.Comment: 21 pages, 11 figures, 7 Table
An Effective Fingerprint Classification and Search Method
This paper presents an effective fingerprint classification method designed
based on a hierarchical agglomerative clustering technique. The performance of
the technique was evaluated in terms of several real-life datasets and a
significant improvement in reducing the misclassification error has been
noticed. This paper also presents a query based faster fingerprint search
method over the clustered fingerprint databases. The retrieval accuracy of the
search method has been found effective in light of several real-life databases.Comment: 10 pages, 8 figures, 6 tables, referred journal publicatio
Two-stage quality adaptive fingerprint image enhancement using Fuzzy c-means clustering based fingerprint quality analysis
Fingerprint recognition techniques are immensely dependent on quality of the
fingerprint images. To improve the performance of recognition algorithm for
poor quality images an efficient enhancement algorithm should be designed.
Performance improvement of recognition algorithm will be more if enhancement
process is adaptive to the fingerprint quality (wet, dry or normal). In this
paper, a quality adaptive fingerprint enhancement algorithm is proposed. The
proposed fingerprint quality assessment algorithm clusters the fingerprint
images in appropriate quality class of dry, wet, normal dry, normal wet and
good quality using fuzzy c-means technique. It considers seven features namely,
mean, moisture, variance, uniformity, contrast, ridge valley area uniformity
and ridge valley uniformity into account for clustering the fingerprint images
in appropriate quality class. Fingerprint images of each quality class undergo
through a two-stage fingerprint quality enhancement process. A quality adaptive
preprocessing method is used as front-end before enhancing the fingerprint
images with Gabor, short term Fourier transform and oriented diffusion
filtering based enhancement techniques. Experimental results show improvement
in the verification results for FVC2004 datasets. Significant improvement in
equal error rate is observed while using quality adaptive preprocessing based
approaches in comparison to the current state-of-the-art enhancement
techniques.Comment: 34 pages, 8 figures, Submitted to Image and Vision Computin
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