1,182 research outputs found
Evaluating software-based fingerprint liveness detection using Convolutional Networks and Local Binary Patterns
With the growing use of biometric authentication systems in the past years,
spoof fingerprint detection has become increasingly important. In this work, we
implement and evaluate two different feature extraction techniques for
software-based fingerprint liveness detection: Convolutional Networks with
random weights and Local Binary Patterns. Both techniques were used in
conjunction with a Support Vector Machine (SVM) classifier. Dataset
Augmentation was used to increase classifier's performance and a variety of
preprocessing operations were tested, such as frequency filtering, contrast
equalization, and region of interest filtering. The experiments were made on
the datasets used in The Liveness Detection Competition of years 2009, 2011 and
2013, which comprise almost 50,000 real and fake fingerprints' images. Our best
method achieves an overall rate of 95.2% of correctly classified samples - an
improvement of 35% in test error when compared with the best previously
published results.Comment: arXiv admin note: text overlap with arXiv:1301.3557 by other author
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
Offline Signature-Based Fuzzy Vault (OSFV: Review and New Results
An offline signature-based fuzzy vault (OSFV) is a bio-cryptographic
implementation that uses handwritten signature images as biometrics instead of
traditional passwords to secure private cryptographic keys. Having a reliable
OSFV implementation is the first step towards automating financial and legal
authentication processes, as it provides greater security of confidential
documents by means of the embedded handwritten signatures. The authors have
recently proposed the first OSFV implementation which is reviewed in this
paper. In this system, a machine learning approach based on the dissimilarity
representation concept is employed to select a reliable feature representation
adapted for the fuzzy vault scheme. Some variants of this system are proposed
for enhanced accuracy and security. In particular, a new method that adapts
user key size is presented. Performance of proposed methods are compared using
the Brazilian PUCPR and GPDS signature databases and results indicate that the
key-size adaptation method achieves a good compromise between security and
accuracy. While average system entropy is increased from 45-bits to about
51-bits, the AER (average error rate) is decreased by about 21%.Comment: This paper has been submitted to The 2014 IEEE Symposium on
Computational Intelligence in Biometrics and Identity Management (CIBIM
Anti-Forensics of Camera Identification and the Triangle Test by Improved Fingerprint-Copy Attack
The fingerprint-copy attack aims to confuse camera identification based on
sensor pattern noise. However, the triangle test shows that the forged images
undergone fingerprint-copy attack would share a non-PRNU (Photo-response
nonuniformity) component with every stolen image, and thus can detect
fingerprint-copy attack. In this paper, we propose an improved fingerprint-copy
attack scheme. Our main idea is to superimpose the estimated fingerprint into
the target image dispersedly, via employing a block-wise method and using the
stolen images randomly and partly. We also develop a practical method to
determine the strength of the superimposed fingerprint based on objective image
quality. In such a way, the impact of non-PRNU component on the triangle test
is reduced, and our improved fingerprint-copy attack is difficultly detected.
The experiments evaluated on 2,900 images from 4 cameras show that our scheme
can effectively fool camera identification, and significantly degrade the
performance of the triangle test simultaneously
Parametic Classification of Handvein Patterns Based on Texture Features
In this paper, we have developed Biometric recognition system adopting hand
based modality Handvein, which has the unique pattern for each individual and
it is impossible to counterfeit and fabricate as it is an internal feature. We
have opted in choosing feature extraction algorithms such as LBP-visual
descriptor ,LPQ-blur insensitive texture operator, Log-Gabor-Texture
descriptor. We have chosen well known classifiers such as KNN and SVM for
classification. We have experimented and tabulated results of single algorithm
recognition rate for Handvein under different distance measures and kernel
options. The feature level fusion is carried out which increased the
performance level.Comment: 8 pages, International Conference on Electrical, Electronics,
Materials and Applied Science (ICEEMAS). AIP: Proceedings International
Conference on Electrical, Electronics, Materials and Applied Science
(ICEEMAS),22nd and 23rd December 201
Group-theoretic structure of linear phase multirate filter banks
Unique lifting factorization results for group lifting structures are used to
characterize the group-theoretic structure of two-channel linear phase FIR
perfect reconstruction filter bank groups. For D-invariant, order-increasing
group lifting structures, it is shown that the associated lifting cascade group
C is isomorphic to the free product of the upper and lower triangular lifting
matrix groups. Under the same hypotheses, the associated scaled lifting group S
is the semidirect product of C by the diagonal gain scaling matrix group D.
These results apply to the group lifting structures for the two principal
classes of linear phase perfect reconstruction filter banks, the whole- and
half-sample symmetric classes. Since the unimodular whole-sample symmetric
class forms a group, W, that is in fact equal to its own scaled lifting group,
W=S_W, the results of this paper characterize the group-theoretic structure of
W up to isomorphism. Although the half-sample symmetric class H does not form a
group, it can be partitioned into cosets of its lifting cascade group, C_H, or,
alternatively, into cosets of its scaled lifting group, S_H. Homomorphic
comparisons reveal that scaled lifting groups covered by the results in this
paper have a structure analogous to a "noncommutative vector space."Comment: 33 pages, 6 figures; to appear in IEEE Transactions on Information
Theor
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
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
Evolutionary Computing and Second generation Wavelet Transform optimization: Current State of the Art
The Evolutionary Computation techniques are exposed to number of domains to achieve optimization. One of those domains is second generation wavelet transformations for image compression. Various types of Lifting Schemes are being introduced in recent literature. Since the growth in Lifting Schemes is in an incremental way and new types of Lifting Schemes are appearing continually. In this context, developing flexible and adaptive optimization approaches is a severe challenge. Evolutionary Computing based lifting scheme optimization techniques are a valuable technology to achieve better results in image compression. However, despite the variety of such methods described in the literature in recent years, security tools incorporating anomaly detection functionalities are just starting to appear, and several important problems remain to be solved. In this paper, we present a review of the most well-known EC approaches for optimizing Secondary level Wavelet transformations
Neural Imaging Pipelines - the Scourge or Hope of Forensics?
Forensic analysis of digital photographs relies on intrinsic statistical
traces introduced at the time of their acquisition or subsequent editing. Such
traces are often removed by post-processing (e.g., down-sampling and
re-compression applied upon distribution in the Web) which inhibits reliable
provenance analysis. Increasing adoption of computational methods within
digital cameras further complicates the process and renders explicit
mathematical modeling infeasible. While this trend challenges forensic analysis
even in near-acquisition conditions, it also creates new opportunities. This
paper explores end-to-end optimization of the entire image acquisition and
distribution workflow to facilitate reliable forensic analysis at the end of
the distribution channel, where state-of-the-art forensic techniques fail. We
demonstrate that a neural network can be trained to replace the entire photo
development pipeline, and jointly optimized for high-fidelity photo rendering
and reliable provenance analysis. Such optimized neural imaging pipeline
allowed us to increase image manipulation detection accuracy from approx. 45%
to over 90%. The network learns to introduce carefully crafted artifacts, akin
to digital watermarks, which facilitate subsequent manipulation detection.
Analysis of performance trade-offs indicates that most of the gains can be
obtained with only minor distortion. The findings encourage further research
towards building more reliable imaging pipelines with explicit
provenance-guaranteeing properties.Comment: Manuscript + supplement; currently under review; compressed figures
to minimize file size. arXiv admin note: text overlap with arXiv:1812.0151
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