3,157 research outputs found
Minutiae Extraction from Fingerprint Images - a Review
Fingerprints are the oldest and most widely used form of biometric
identification. Everyone is known to have unique, immutable fingerprints. As
most Automatic Fingerprint Recognition Systems are based on local ridge
features known as minutiae, marking minutiae accurately and rejecting false
ones is very important. However, fingerprint images get degraded and corrupted
due to variations in skin and impression conditions. Thus, image enhancement
techniques are employed prior to minutiae extraction. A critical step in
automatic fingerprint matching is to reliably extract minutiae from the input
fingerprint images. This paper presents a review of a large number of
techniques present in the literature for extracting fingerprint minutiae. The
techniques are broadly classified as those working on binarized images and
those that work on gray scale images directly.Comment: 12 pages; IJCSI International Journal of Computer Science Issues,
Vol. 8, Issue 5, September 201
Automated Latent Fingerprint Recognition
Latent fingerprints are one of the most important and widely used evidence in
law enforcement and forensic agencies worldwide. Yet, NIST evaluations show
that the performance of state-of-the-art latent recognition systems is far from
satisfactory. An automated latent fingerprint recognition system with high
accuracy is essential to compare latents found at crime scenes to a large
collection of reference prints to generate a candidate list of possible mates.
In this paper, we propose an automated latent fingerprint recognition algorithm
that utilizes Convolutional Neural Networks (ConvNets) for ridge flow
estimation and minutiae descriptor extraction, and extract complementary
templates (two minutiae templates and one texture template) to represent the
latent. The comparison scores between the latent and a reference print based on
the three templates are fused to retrieve a short candidate list from the
reference database. Experimental results show that the rank-1 identification
accuracies (query latent is matched with its true mate in the reference
database) are 64.7% for the NIST SD27 and 75.3% for the WVU latent databases,
against a reference database of 100K rolled prints. These results are the best
among published papers on latent recognition and competitive with the
performance (66.7% and 70.8% rank-1 accuracies on NIST SD27 and WVU DB,
respectively) of a leading COTS latent Automated Fingerprint Identification
System (AFIS). By score-level (rank-level) fusion of our system with the
commercial off-the-shelf (COTS) latent AFIS, the overall rank-1 identification
performance can be improved from 64.7% and 75.3% to 73.3% (74.4%) and 76.6%
(78.4%) on NIST SD27 and WVU latent databases, respectively
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
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
An Effective Fingerprint Verification Technique
This paper presents an effective method for fingerprint verification based on
a data mining technique called minutiae clustering and a graph-theoretic
approach to analyze the process of fingerprint comparison to give a feature
space representation of minutiae and to produce a lower bound on the number of
detectably distinct fingerprints. The method also proving the invariance of
each individual fingerprint by using both the topological behavior of the
minutiae graph and also using a distance measure called Hausdorff distance.The
method provides a graph based index generation mechanism of fingerprint
biometric data. The self-organizing map neural network is also used for
classifying the fingerprints.Comment: Submitted to Journal of Computer Science and Engineering, see
http://sites.google.com/site/jcseuk/volume-1-issue-1-may-201
Skilled Impostor Attacks Against Fingerprint Verification Systems And Its Remedy
Fingerprint verification systems are becoming ubiquitous in everyday life.
This trend is propelled especially by the proliferation of mobile devices with
fingerprint sensors such as smartphones and tablet computers, and fingerprint
verification is increasingly applied for authenticating financial transactions.
In this study we describe a novel attack vector against fingerprint
verification systems which we coin skilled impostor attack. We show that
existing protocols for performance evaluation of fingerprint verification
systems are flawed and as a consequence of this, the system's real
vulnerability is systematically underestimated. We examine a scenario in which
a fingerprint verification system is tuned to operate at false acceptance rate
of 0.1% using the traditional verification protocols with random impostors
(zero-effort attacks). We demonstrate that an active and intelligent attacker
can achieve a chance of success in the area of 89% or more against this system
by performing skilled impostor attacks. We describe a new protocol for
evaluating fingerprint verification performance in order to improve the
assessment of potential and limitations of fingerprint recognition systems.
This new evaluation protocol enables a more informed decision concerning the
operating threshold in practical applications and the respective trade-off
between security (low false acceptance rates) and usability (low false
rejection rates). The skilled impostor attack is a general attack concept which
is independent of specific databases or comparison algorithms. The proposed
protocol relying on skilled impostor attacks can directly be applied for
evaluating the verification performance of other biometric modalities such as
e.g. iris, face, ear, finger vein, gait or speaker recognition
A Stable Minutia Descriptor based on Gabor Wavelet and Linear Discriminant Analysis
The minutia descriptor which describes characteristics of minutia, plays a
major role in fingerprint recognition. Typically, fingerprint recognition
systems employ minutia descriptors to find potential correspondence between
minutiae, and they use similarity between two minutia descriptors to calculate
overall similarity between two fingerprint images. A good minutia descriptor
can improve recognition accuracy of fingerprint recognition system and largely
reduce comparing time. A good minutia descriptor should have high ability to
distinguish between different minutiae and at the same time should be robust in
difficult conditions including poor quality image and small size image. It also
should be effective in computational cost of similarity among descriptors. In
this paper, a robust minutia descriptor is constructed using Gabor wavelet and
linear discriminant analysis. This minutia descriptor has high distinguishing
ability, stability and simple comparing method. Experimental results on FVC2004
and FVC2006 databases show that the proposed minutia descriptor is very
effective in fingerprint recognition
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
Portable Trust: biometric-based authentication and blockchain storage for self-sovereign identity systems
We devised a mobile biometric-based authentication system only relying on
local processing. Our Android open source solution explores the capability of
current smartphones to acquire, process and match fingerprints using only its
built-in hardware. Our architecture is specifically designed to run completely
locally and autonomously, not requiring any cloud service, server, or
permissioned access to fingerprint reader hardware. It involves three main
stages, starting with the fingerprint acquisition using the smartphone camera,
followed by a processing pipeline to obtain minutiae features and a final step
for matching against other locally stored fingerprints, based on Oriented FAST
and Rotated BRIEF (ORB) descriptors. We obtained a mean matching accuracy of
55%, with the highest value of 67% for thumb fingers. Our ability to capture
and process a finger fingerprint in mere seconds using a smartphone makes this
work usable in a wide range of scenarios, for instance, offline remote regions.
This work is specifically designed to be a key building block for a
self-sovereign identity solution and integrate with our permissionless
blockchain for identity and key attestation.Comment: Delft University of Technology student project repor
Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier
Prevailing fingerprint recognition systems are vulnerable to spoof attacks.
To mitigate these attacks, automated spoof detectors are trained to distinguish
a set of live or bona fide fingerprints from a set of known spoof fingerprints.
Despite their success, spoof detectors remain vulnerable when exposed to
attacks from spoofs made with materials not seen during training of the
detector. To alleviate this shortcoming, we approach spoof detection as a
one-class classification problem. The goal is to train a spoof detector on only
the live fingerprints such that once the concept of "live" has been learned,
spoofs of any material can be rejected. We accomplish this through training
multiple generative adversarial networks (GANS) on live fingerprint images
acquired with the open source, dual-camera, 1900 ppi RaspiReader fingerprint
reader. Our experimental results, conducted on 5.5K spoof images (from 12
materials) and 11.8K live images show that the proposed approach improves the
cross-material spoof detection performance over state-of-the-art one-class and
binary class spoof detectors on 11 of 12 testing materials and 7 of 12 testing
materials, respectively
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