3,520 research outputs found
Global Variational Method for Fingerprint Segmentation by Three-part Decomposition
Verifying an identity claim by fingerprint recognition is a commonplace
experience for millions of people in their daily life, e.g. for unlocking a
tablet computer or smartphone. The first processing step after fingerprint
image acquisition is segmentation, i.e. dividing a fingerprint image into a
foreground region which contains the relevant features for the comparison
algorithm, and a background region. We propose a novel segmentation method by
global three-part decomposition (G3PD). Based on global variational analysis,
the G3PD method decomposes a fingerprint image into cartoon, texture and noise
parts. After decomposition, the foreground region is obtained from the non-zero
coefficients in the texture image using morphological processing. The
segmentation performance of the G3PD method is compared to five
state-of-the-art methods on a benchmark which comprises manually marked ground
truth segmentation for 10560 images. Performance evaluations show that the G3PD
method consistently outperforms existing methods in terms of segmentation
accuracy
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
Parallel Stroked Multi Line: a model-based method for compressing large fingerprint databases
With increasing usage of fingerprints as an important biometric data, the
need to compress the large fingerprint databases has become essential. The most
recommended compression algorithm, even by standards, is JPEG2K. But at high
compression rates, this algorithm is ineffective. In this paper, a model is
proposed which is based on parallel lines with same orientations, arbitrary
widths and same gray level values located on rectangle with constant gray level
value as background. We refer to this algorithm as Parallel Stroked Multi Line
(PSML). By using Adaptive Geometrical Wavelet and employing PSML, a compression
algorithm is developed. This compression algorithm can preserve fingerprint
structure and minutiae. The exact algorithm of computing the PSML model take
exponential time. However, we have proposed an alternative approximation
algorithm, which reduces the time complexity to . The proposed PSML
alg. has significant advantage over Wedgelets Transform in PSNR value and
visual quality in compressed images. The proposed method, despite the lower
PSNR values than JPEG2K algorithm in common range of compression rates, in all
compression rates have nearly equal or greater advantage over JPEG2K when used
by Automatic Fingerprint Identification Systems (AFIS). At high compression
rates, according to PSNR values, mean EER rate and visual quality, the encoded
images with JPEG2K can not be identified from each other after compression.
But, images encoded by the PSML alg. retained the sufficient information to
maintain fingerprint identification performances similar to the ones obtained
by raw images without compression. One the U.are.U 400 database, the mean EER
rate for uncompressed images is 4.54%, while at 267:1 compression ratio, this
value becomes 49.41% and 6.22% for JPEG2K and PSML, respectively. This result
shows a significant improvement over the standard JPEG2K algorithm.Comment: 26 pages, 10 figures, submitted to Computer Vision and Image
Understandin
An Innovative Scheme For Effectual Fingerprint Data Compression Using Bezier Curve Representations
Naturally, with the mounting application of biometric systems, there arises a
difficulty in storing and handling those acquired biometric data. Fingerprint
recognition has been recognized as one of the most mature and established
technique among all the biometrics systems. In recent times, with fingerprint
recognition receiving increasingly more attention the amount of fingerprints
collected has been constantly creating enormous problems in storage and
transmission. Henceforth, the compression of fingerprints has emerged as an
indispensable step in automated fingerprint recognition systems. Several
researchers have presented approaches for fingerprint image compression. In
this paper, we propose a novel and efficient scheme for fingerprint image
compression. The presented scheme utilizes the Bezier curve representations for
effective compression of fingerprint images. Initially, the ridges present in
the fingerprint image are extracted along with their coordinate values using
the approach presented. Subsequently, the control points are determined for all
the ridges by visualizing each ridge as a Bezier curve. The control points of
all the ridges determined are stored and are used to represent the fingerprint
image. When needed, the fingerprint image is reconstructed from the stored
control points using Bezier curves. The quality of the reconstructed
fingerprint is determined by a formal evaluation. The proposed scheme achieves
considerable memory reduction in storing the fingerprint.Comment: 9 pages IEEE format, International Journal of Computer Science and
Information Security, IJCSIS 2009, ISSN 1947 5500, Impact Factor 0.423,
http://sites.google.com/site/ijcsis
Improving Iris Recognition Accuracy By Score Based Fusion Method
Iris recognition technology, used to identify individuals by photographing
the iris of their eye, has become popular in security applications because of
its ease of use, accuracy, and safety in controlling access to high-security
areas. Fusion of multiple algorithms for biometric verification performance
improvement has received considerable attention. The proposed method combines
the zero-crossing 1 D wavelet Euler number, and genetic algorithm based for
feature extraction. The output from these three algorithms is normalized and
their score are fused to decide whether the user is genuine or imposter. This
new strategies is discussed in this paper, in order to compute a multimodal
combined score.Comment: http://ijict.org/index.php/ijoat/article/view/improving-iris-recognitio
Filter Design and Performance Evaluation for Fingerprint Image Segmentation
Fingerprint recognition plays an important role in many commercial
applications and is used by millions of people every day, e.g. for unlocking
mobile phones. Fingerprint image segmentation is typically the first processing
step of most fingerprint algorithms and it divides an image into foreground,
the region of interest, and background. Two types of error can occur during
this step which both have a negative impact on the recognition performance:
'true' foreground can be labeled as background and features like minutiae can
be lost, or conversely 'true' background can be misclassified as foreground and
spurious features can be introduced. The contribution of this paper is
threefold: firstly, we propose a novel factorized directional bandpass (FDB)
segmentation method for texture extraction based on the directional Hilbert
transform of a Butterworth bandpass (DHBB) filter interwoven with
soft-thresholding. Secondly, we provide a manually marked ground truth
segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a
systematic performance comparison between the FDB method and four of the most
often cited fingerprint segmentation algorithms showing that the FDB
segmentation method clearly outperforms these four widely used methods. The
benchmark and the implementation of the FDB method are made publicly available
Simultaneous Inpainting and Denoising by Directional Global Three-part Decomposition: Connecting Variational and Fourier Domain Based Image Processing
We consider the very challenging task of restoring images (i) which have a
large number of missing pixels, (ii) whose existing pixels are corrupted by
noise and (iii) the ideal image to be restored contains both cartoon and
texture elements. The combination of these three properties makes this inverse
problem a very difficult one. The solution proposed in this manuscript is based
on directional global three-part decomposition (DG3PD) [ThaiGottschlich2016]
with directional total variation norm, directional G-norm and
-norm in curvelet domain as key ingredients of the model. Image
decomposition by DG3PD enables a decoupled inpainting and denoising of the
cartoon and texture components. A comparison to existing approaches for
inpainting and denoising shows the advantages of the proposed method. Moreover,
we regard the image restoration problem from the viewpoint of a Bayesian
framework and we discuss the connections between the proposed solution by
function space and related image representation by harmonic analysis and
pyramid decomposition
Directional Global Three-part Image Decomposition
We consider the task of image decomposition and we introduce a new model
coined directional global three-part decomposition (DG3PD) for solving it. As
key ingredients of the DG3PD model, we introduce a discrete multi-directional
total variation norm and a discrete multi-directional G-norm. Using these novel
norms, the proposed discrete DG3PD model can decompose an image into two parts
or into three parts. Existing models for image decomposition by Vese and Osher,
by Aujol and Chambolle, by Starck et al., and by Thai and Gottschlich are
included as special cases in the new model. Decomposition of an image by DG3PD
results in a cartoon image, a texture image and a residual image. Advantages of
the DG3PD model over existing ones lie in the properties enforced on the
cartoon and texture images. The geometric objects in the cartoon image have a
very smooth surface and sharp edges. The texture image yields oscillating
patterns on a defined scale which is both smooth and sparse. Moreover, the
DG3PD method achieves the goal of perfect reconstruction by summation of all
components better than the other considered methods. Relevant applications of
DG3PD are a novel way of image compression as well as feature extraction for
applications such as latent fingerprint processing and optical character
recognition
Secure Iris Authentication Using Visual Cryptography
Biometrics deal with automated methods of identifying a person or verifying
the identity of a person based on physiological or behavioral characteristics.
Visual cryptography is a secret sharing scheme where a secret image is
encrypted into the shares which independently disclose no information about the
original secret image. As biometric template are stored in the centralized
database, due to security threats biometric template may be modified by
attacker. If biometric template is altered authorized user will not be allowed
to access the resource. To deal this issue visual cryptography schemes can be
applied to secure the iris template. Visual cryptography provides great means
for helping such security needs as well as extra layer of authentication.Comment: IEEE Publication format, ISSN 1947 5500,
http://sites.google.com/site/ijcsis
ECG Identification under Exercise and Rest Situations via Various Learning Methods
As the advancement of information security, human recognition as its core
technology, has absorbed an increasing amount of attention in the past few
years. A myriad of biometric features including fingerprint, face, iris, have
been applied to security systems, which are occasionally considered vulnerable
to forgery and spoofing attacks. Due to the difficulty of being fabricated,
electrocardiogram (ECG) has attracted much attention. Though many works have
shown the excellent human identification provided by ECG, most current ECG
human identification (ECGID) researches only focus on rest situation. In this
manuscript, we overcome the oversimplification of previous researches and
evaluate the performance under both exercise and rest situations, especially
the influence of exercise on ECGID. By applying various existing learning
methods to our ECG dataset, we find that current methods which can well support
the identification of individuals under rests, do not suffice to present
satisfying ECGID performance under exercise situations, therefore exposing the
deficiency of existing ECG identification methods
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