9,608 research outputs found
Alignment-Free Cross-Sensor Fingerprint Matching based on the Co-Occurrence of Ridge Orientations and Gabor-HoG Descriptor
The existing automatic fingerprint verification methods are designed to work
under the assumption that the same sensor is installed for enrollment and
authentication (regular matching). There is a remarkable decrease in efficiency
when one type of contact-based sensor is employed for enrolment and another
type of contact-based sensor is used for authentication (cross-matching or
fingerprint sensor interoperability problem,). The ridge orientation patterns
in a fingerprint are invariant to sensor type. Based on this observation, we
propose a robust fingerprint descriptor called the co-occurrence of ridge
orientations (Co-Ror), which encodes the spatial distribution of ridge
orientations. Employing this descriptor, we introduce an efficient automatic
fingerprint verification method for cross-matching problem. Further, to enhance
the robustness of the method, we incorporate scale based ridge orientation
information through Gabor-HoG descriptor. The two descriptors are fused with
canonical correlation analysis (CCA), and the matching score between two
fingerprints is calculated using city-block distance. The proposed method is
alignment-free and can handle the matching process without the need for a
registration step. The intensive experiments on two benchmark databases
(FingerPass and MOLF) show the effectiveness of the method and reveal its
significant enhancement over the state-of-the-art methods such as VeriFinger (a
commercial SDK), minutia cylinder-code (MCC), MCC with scale, and the
thin-plate spline (TPS) model. The proposed research will help security
agencies, service providers and law-enforcement departments to overcome the
interoperability problem of contact sensors of different technology and
interaction types
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
A Fully Automated Latent Fingerprint Matcher with Embedded Self-learning Segmentation Module
Latent fingerprint has the practical value to identify the suspects who have
unintentionally left a trace of fingerprint in the crime scenes. However,
designing a fully automated latent fingerprint matcher is a very challenging
task as it needs to address many challenging issues including the separation of
overlapping structured patterns over the partial and poor quality latent
fingerprint image, and finding a match against a large background database that
would have different resolutions. Currently there is no fully automated latent
fingerprint matcher available to the public and most literature reports have
utilized a specialized latent fingerprint matcher COTS3 which is not accessible
to the public. This will make it infeasible to assess and compare the relevant
research work which is vital for this research community. In this study, we
target to develop a fully automated latent matcher for adaptive detection of
the region of interest and robust matching of latent prints. Unlike the
manually conducted matching procedure, the proposed latent matcher can run like
a sealed black box without any manual intervention. This matcher consists of
the following two modules: (i) the dictionary learning-based region of interest
(ROI) segmentation scheme; and (ii) the genetic algorithm-based minutiae set
matching unit. Experimental results on NIST SD27 latent fingerprint database
demonstrates that the proposed matcher outperforms the currently public
state-of-art latent fingerprint matcher
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
Performance Measurement and Method Analysis (PMMA) for Fingerprint Reconstruction
Fingerprint reconstruction is one of the most well-known and publicized
biometrics. Because of their uniqueness and consistency over time, fingerprints
have been used for identification over a century, more recently becoming
automated due to advancements in computed capabilities. Fingerprint
reconstruction is popular because of the inherent ease of acquisition, the
numerous sources (e.g. ten fingers) available for collection, and their
established use and collections by law enforcement and immigration.
Fingerprints have always been the most practical and positive means of
identification. Offenders, being well aware of this, have been coming up with
ways to escape identification by that means. Erasing left over fingerprints,
using gloves, fingerprint forgery; are certain examples of methods tried by
them, over the years. Failing to prevent themselves, they moved to an extent of
mutilating their finger skin pattern, to remain unidentified. This article is
based upon obliteration of finger ridge patterns and discusses some known cases
in relation to the same, in chronological order; highlighting the reasons why
offenders go to an extent of performing such act. The paper gives an overview
of different methods and performance measurement of the fingerprint
reconstruction.Comment: 4pages,1 figure,1 tabl
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
Palmprint image registration using convolutional neural networks and Hough transform
Minutia-based palmprint recognition systems has got lots of interest in last
two decades. Due to the large number of minutiae in a palmprint, approximately
1000 minutiae, the matching process is time consuming which makes it
unpractical for real time applications. One way to address this issue is
aligning all palmprint images to a reference image and bringing them to a same
coordinate system. Bringing all palmprint images to a same coordinate system,
results in fewer computations during minutia matching. In this paper, using
convolutional neural network (CNN) and generalized Hough transform (GHT), we
propose a new method to register palmprint images accurately. This method,
finds the corresponding rotation and displacement (in both x and y direction)
between the palmprint and a reference image. Exact palmprint registration can
enhance the speed and the accuracy of matching process. Proposed method is
capable of distinguishing between left and right palmprint automatically which
helps to speed up the matching process. Furthermore, designed structure of CNN
in registration stage, gives us the segmented palmprint image from background
which is a pre-processing step for minutia extraction. The proposed
registration method followed by minutia-cylinder code (MCC) matching algorithm
has been evaluated on the THUPALMLAB database, and the results show the
superiority of our algorithm over most of the state-of-the-art algorithms.Comment: 6 figures, 8 page
Fingerprint Recognition Using Minutia Score Matching
The popular Biometric used to authenticate a person is Fingerprint which is
unique and permanent throughout a person's life. A minutia matching is widely
used for fingerprint recognition and can be classified as ridge ending and
ridge bifurcation. In this paper we projected Fingerprint Recognition using
Minutia Score Matching method (FRMSM). For Fingerprint thinning, the Block
Filter is used, which scans the image at the boundary to preserves the quality
of the image and extract the minutiae from the thinned image. The false
matching ratio is better compared to the existing algorithm.Comment: 8 Page
Comparison of fingerprint authentication algorithms for small imaging sensors
The demand for biometric systems has been increasing with the growth of the
smartphone market. Biometric devices allow the user to authenticate easily
while securing its private data without the need to remember any access code.
Amongst them, fingerprint sensors are the most widespread because they seem to
provide a good balance between reliability, cost and ease of use. According to
smartphone manufacturers, the security level is guaranteed to be high. However,
the size of those sensors, which is only a few millimeters squared, prevents
the use of minutiae algorithms. To the best of our knowledge, very few studies
shed light onto this problem, though many pattern recognition algorithms
already exist as well as commercial solutions which are supposedly robust. In
this article we try to provide insights on how to tackle this problem by
analyzing the performance of three algorithms dedicated to pattern recognition.Comment: On going work which will be improved with more experimental result
A non-invertible cancelable fingerprint template generation based on ridge feature transformation
In a biometric verification system, leakage of biometric data leads to
permanent identity loss since original biometric data is inherently linked to a
user. Further, various types of attacks on a biometric system may reveal the
original template and utility in other applications. To address these security
and privacy concerns cancelable biometric has been introduced. Cancelable
biometric constructs a protected template from the original biometric template
using transformation functions and performs the comparison between templates in
the transformed domain. Recent approaches towards cancelable fingerprint
generation either rely on aligning minutiae points with respect to singular
points (core/delta) or utilize the absolute coordinate positions of minutiae
points. In this paper, we propose a novel non-invertible ridge feature
transformation method to protect the original fingerprint template information.
The proposed method partitions the fingerprint region into a number of sectors
with reference to each minutia point employing a ridge-based co-ordinate
system. The nearest neighbor minutiae in each sector are identified, and
ridge-based features are computed. Further, a cancelable template is generated
by applying the Cantor pairing function followed by random projection. We have
evaluated our method with FVC2002, FVC2004 and FVC2006 databases. It is evident
from the experimental results that the proposed method outperforms existing
methods in the literature. Moreover, the security analysis demonstrates that
the proposed method fulfills the necessary requirements of non-invertibility,
revocability, and diversity with a minor performance degradation caused due to
cancelable transformation
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