910 research outputs found
Performance of the Fuzzy Vault for Multiple Fingerprints (Extended Version)
The fuzzy vault is an error tolerant authentication method that ensures the
privacy of the stored reference data. Several publications have proposed the
application of the fuzzy vault to fingerprints, but the results of subsequent
analyses indicate that a single finger does not contain sufficient information
for a secure implementation. In this contribution, we present an implementation
of a fuzzy vault based on minutiae information in several fingerprints aiming
at a security level comparable to current cryptographic applications. We
analyze and empirically evaluate the security, efficiency, and robustness of
the construction and several optimizations. The results allow an assessment of
the capacity of the scheme and an appropriate selection of parameters. Finally,
we report on a practical simulation conducted with ten users.Comment: This article represents the full paper of a short version to appear
in the Proceedings of BIOSIG 2010 (copyright of Gesellschaft f\"ur
Informatik
FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction
Minutiae extraction is of critical importance in automated fingerprint
recognition. Previous works on rolled/slap fingerprints failed on latent
fingerprints due to noisy ridge patterns and complex background noises. In this
paper, we propose a new way to design deep convolutional network combining
domain knowledge and the representation ability of deep learning. In terms of
orientation estimation, segmentation, enhancement and minutiae extraction,
several typical traditional methods performed well on rolled/slap fingerprints
are transformed into convolutional manners and integrated as an unified plain
network. We demonstrate that this pipeline is equivalent to a shallow network
with fixed weights. The network is then expanded to enhance its representation
ability and the weights are released to learn complex background variance from
data, while preserving end-to-end differentiability. Experimental results on
NIST SD27 latent database and FVC 2004 slap database demonstrate that the
proposed algorithm outperforms the state-of-the-art minutiae extraction
algorithms. Code is made publicly available at:
https://github.com/felixTY/FingerNet
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
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
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
Minutia Texture Cylinder Codes for fingerprint matching
Minutia Cylinder Codes (MCC) are minutiae based fingerprint descriptors that
take into account minutiae information in a fingerprint image for fingerprint
matching. In this paper, we present a modification to the underlying
information of the MCC descriptor and show that using different features, the
accuracy of matching is highly affected by such changes. MCC originally being a
minutia only descriptor is transformed into a texture descriptor. The
transformation is from minutiae angular information to orientation, frequency
and energy information using Short Time Fourier Transform (STFT) analysis. The
minutia cylinder codes are converted to minutiae texture cylinder codes (MTCC).
Based on a fixed set of parameters, the proposed changes to MCC show improved
performance on FVC 2002 and 2004 data sets and surpass the traditional MCC
performance
Bio-Authentication based Secure Transmission System using Steganography
Biometrics deals with identity verification of an individual by using certain
physiological or behavioral features associated with a person. Biometric
identification systems using fingerprints patterns are called AFIS (Automatic
Fingerprint Identification System). In this paper a composite method for
Fingerprint recognition is considered using a combination of Fast Fourier
Transform (FFT) and Sobel Filters for improvement of a poor quality fingerprint
image. Steganography hides messages inside other messages in such a way that an
"adversary" would not even know a secret message were present. The objective of
our paper is to make a bio-secure system. In this paper bio-authentication has
been implemented in terms of finger print recognition and the second part of
the paper is an interactive steganographic system hides the user's data by two
options- creating a songs list or hiding the data in an image.Comment: IEEE Publication format, International Journal of Computer Science
and Information Security, IJCSIS, Vol. 8 No. 1, April 2010, USA. ISSN 1947
5500, http://sites.google.com/site/ijcsis
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
Generation of Biometric key for use in DES
Cryptography is an important field in the area of data encryption. There are
different cryptographic techniques available varying from the simplest to
complex. One of the complex symmetric key cryptography techniques is using Data
Encryption Standard Algorithm. This paper explores a unique approach to
generation of key using fingerprint. The generated key is used as an input key
to the DES Algorith
Persistent homology machine learning for fingerprint classification
The fingerprint classification problem is to sort fingerprints into
pre-determined groups, such as arch, loop, and whorl. It was asserted in the
literature that minutiae points, which are commonly used for fingerprint
matching, are not useful for classification. We show that, to the contrary,
near state-of-the-art classification accuracy rates can be achieved when
applying topological data analysis (TDA) to 3-dimensional point clouds of
oriented minutiae points. We also apply TDA to fingerprint ink-roll images,
which yields a lower accuracy rate but still shows promise, particularly since
the only preprocessing is cropping; moreover, combining the two approaches
outperforms each one individually. These methods use supervised learning
applied to persistent homology and allow us to explore feature selection on
barcodes, an important topic at the interface between TDA and machine learning.
We test our classification algorithms on the NIST fingerprint database SD-27.Comment: 15 page
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