thesis presentation
TRANSCRIPT
Presented By: Kareem Kamal A. Ghany Under Supervision of: Prof. Aboul Ella Hassanien Prof. Hesham A. Hefny
4, June 2014
INTRODUCTION
Problem Definition
Research Objective
Thesis Structure
OVERVIEW
Biometrics
Machine Learning
Related Work
PROPOSED APPROACH
A Symmetric Bio-Hash Function Based on Fingerprint
Minutiae and Principal Curves Approach
Hybrid Approach for Protecting Biometrics Template
Watermarking effect on the Biometric System
CONCLUSION
FUTURE WORK Kareem Kamal A.Ghany 2
The main problem of this research work is to
find a general way to make a hybrid
biometric system based on machine learning
techniques more effective in a broader range
of applications for increasing performance of
security systems.
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Provide an intelligent hybrid
biometrics system that uses a
cryptosystem approach combined
with a transformation based
biometric approach
Prove that the watermarking
technology does not affect the
performance of the biometric
system
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To increase the performance of the security system.
To decrease the error rate in the biometric system.
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Ch2: Biometrics: An overview
Ch3: Machine Learning techniques : An overview
Ch4: Proposed Unimodal Biometric approach
Ch5: Proposed Hybrid Biometric approach
Ch6: Proposed watermarking approach based on DNA and Fingerprint
Ch7: Conclusion & Future work
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A biometric is a unique, measurable characteristic or trait of a human being for
automatically recognizing or verifying their identity.
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The September 11 Terrorist Attacks
Following the September 11 attacks (2001), The use of biometrics is likely to increase in the future as security concerns become more of a priority for both governments and corporations.
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Only biometrics can verify you as you
Tokens (smartcards, etc.) aren't you and can be: lost stolen duplicated (some) forgotten
Passwords aren't you and can be: forgotten shared observed broken
A multi-biometric system can be classified into one of the following six categories:
multi-sensor, multi-algorithm, multi-instance, multi-sample, multi-modal, or hybrid.
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Hybrid systems: The term hybrid used to
describe systems that integrate a subset of
the other five scenarios. For example,
discussing an arrangement in which
fingerprint recognition algorithm id
combined with DNA recognition algorithm
at the match score.
Physical biometrics Behavioral biometrics
Examples:
- Speaker recognition.
- Signature recognition.
- Gait recognition.
- Keystroke dynamics.
Examples:
-Fingerprint.
- Iris print.
- Face print.
-DNA print.
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- It’s how the computer (machine) would automatically extract the algorithm for any
task .
- The research is aim to illustrate how ML techniques can be applied to solve
biometrics problems and how biometrics data can be analyzed, processed, and
classified by ML.
- Example of ML techniques:
Neural Networks Kernel Methods
Support Vector Machine (SVM) Near Sets
Fuzzy sets Principal Component Analysis (PCA)
Genatic Algorithm (GA) Rough Sets
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It is a way of identifying patterns in data,
and expressing the data in
such a way as to highlight their similarities
and differences.
PCA is a powerful tool for analyzing data.
It can be used for feature extraction
purposes in its special case (Principal
Curves Approach)
In (Juels and Sudan, 2002) they proposed a fuzzy vault scheme which embeds a
secret S in a fuzzy vault with a dataset A. In order to extract the secret, one needs to
present another set B to decrypt the vault.
In (Cheung et al., 2005) authors revealed that noninvertible process can be invertible
to the extent that the matcher may be fooled through a case study on bio-hashing and
face.
In (Uludag et al., 2005) they proposed a model to explore the realization of a
cryptographic construct, with the fingerprint minutiae data. This construct aims to
secure critical data such as secret encryption key with the fingerprint data in a way
that only the authorized user can access the secret by providing the valid fingerprint.
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Cancelable biometrics was first proposed by (Ratha et al., 2006), An improvement
for their work is presented through accurate registration which is a key step in
building cancelable transforms, the overall approach has been tested using large
databases and the results demonstrate that without losing much accuracy a large
number of cancelable transforms for fingerprints can be built.
In (Feng et al., 2008), they proposed a three-step cancelable framework which is a
hybrid approach for face template protection. This hybrid algorithm is based on the
random projection, class distribution preserving transform and hash function.
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In (Kumar and Vikram, 2010) they proposed a new method of recognition of the
pattern of distribution of the minutiae points of an enhanced image, and they used a
multi-dimensional artificial neural network. the advantage of their method is the
usage of the entire resized minutiae image as an input at once. It is capable of
excellent pattern recognition properties as the distribution of the minutiae points are
used directly for recognition.
In (Nagar et al., 2010) they proposed a hybrid approach that improves the matching
performance as well as security of the fuzzy vault, one limitation of a fuzzy
commitment scheme designed using typical algebraic codes is that these codes
do not meet the Hamming bound.
In (Kekre et al., 2010) they proposed image retrieval techniques based on features
extracted from Kekre transform and then applied them on row mean, column mean
and combination.
17
1:Proposed Uni-modal Biometric approach
2:Proposed Hybrid Biometric approach
3:Proposed watermarking approach based on
DNA and Fingerprint
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1:Proposed Uni-modal Biometric approach
2:Proposed Hybrid Biometric approach
3:Proposed watermarking approach based on
DNA and Fingerprint
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Minutiae Types
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Same Fingerprints with Different Positions
Securing Fingerprint Template
We use bio-hash function as a Unimodal Biometric System to secure the fingerprint template as showing in this Figure Put Algorithm after this slide
Uni-modal Biometric Approach
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Shifting of minutia points
The transformation done from one fingerprint to another can be described by the following equation: Where z represents the minutia point, r represents the minutia rotation, and t represents the minutia translation, this Figure shows an example of shifting the minutia points.
Proposed Uni-modal Approach
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Where: r is the rotation.
t is the translation
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Large distance between the points
tends to large error rate
To Minimize the error rate we will
apply the Hybrid Approach (next
contribution)
A.GHANY Kareem Kamal, et. al, ”A Symmetric Bio-Hash Function Based On Fingerprint Minutiae
And Principal Curves Approach”, The 3rd International Conference on Mechanical and Electrical
Technology ( ICMET 2011), ASME, China, vol. 1, 2011.
1:Proposed Uni-modal Biometric approach
2:Proposed Hybrid Biometric approach
3:Proposed watermarking approach based on
DNA and Fingerprint
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Class Distribution Preserving (CDP) is a technique used for template transformation
by using a set of ”distinguish point” and distance measurement. The ”distinguish
point” set is randomly generated, which will increase the randomness of the
representation. CDP transfers a real value template into a binary template. The
main idea is to make use of the group of distinguishing points, a distance function
and the threshold.
Proposed
Hybrid
Approach
Transformation
Approach
Biometric
Cryptosystem
Approach C
DP
Tra
nsf
orm
Sym
met
ric
Has
h
Fu
nct
ion
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Where: r is the rotation.
t is the translation
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A.GHANY Kareem Kamal, et. al, ”A Hybrid approach for biometric template security”, The 2012
IEEE/ACM international conference on Advances in Social Network Analysis and Mining
(ASONAM 2012), Turkey, pp. 941-942, 2012.
Kekre’s transform is a technique used for template transformation which include in
its traditional methods the template that transformed using the user’s password
during the enrollment, and the authentication.
Proposed
Hybrid
Approach
Transformation
Approach
Biometric
Cryptosystem
Approach K
ekre
’s
Tra
nsf
orm
Sym
met
ric
Has
h F
un
ctio
n
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Our new proposed approach contains four phases: A. Feature extraction using principal curves approach. B. Kekre’s transform. C. Securing the template by using Bio-Hash function. D. Matching Phase.
During authentication, the query is used to recover
the original biometric template from the secure
sketch and the exact recovery of the original
biometric data is verified to authenticate a user.
Also, new hash values are produced by the
scanner and are matched with those stored in the
database.
We find that matching can be performed on both
the client side and on the server side, and it is
performed using hashed features instead of the
original template.
Authentication is depending on scores which can
range between percentage from 0% to 100%.
Hybrid Biometric Approach Kekre’s transform
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Kekre’s transform Vs. Class Distribution Preserving (CDP)
CASIA database gave the accurate results of 85% which is higher than CDP, shows
that using Kekre transform is better than CDP transform by 20%.
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Where: r is the rotation.
t is the translation
E in [2] is the error rate when using CDP transform.
E in new approach is the error rate when using Kekre’s transform.
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A.GHANY Kareem Kamal, et. al, ”Kekre’s transform for protecting fingerprint template”, The 13th
International Conference on Hybrid Intelligent Systems (HIS 2013), IEEE, Tunisia, pp. 186-191,
2013.
1:Proposed Uni-modal Biometric approach
2:Proposed Hybrid Biometric approach
3:Proposed watermarking approach based on
DNA and Fingerprint
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Performance measurements of Fingerprint Image and DNA
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DNA typing is very useful in crime detection.
Since DNA requires a form of blood, tissue, or other bodily sample, it has not
yet been adopted as a major biometrics methods, even though it is now
possible to analyze human DNA within 10 minutes.
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Digital watermarking methods
should be imperceptible in order
to be effective, while at the same
time robust to common image
manipulations like rotation,
compression, scaling, filtering,
cropping, and collusion attacks
through other signal processing
operations.
Digital Image Watermarking
Spatial
Domain
Frequency Domain
DCT DFT DWT
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The hierarchical property of the DWT offers the possibility of analyzing a
signal at different resolutions (levels) and orientation. This multi resolutions
analysis gives both space and frequency localization, and different
orientations extract different features of the frame, such as vertical, horizontal
and diagonal information. The DWT of an image has two parts: an
approximation part (this is an image with smaller dimensions) and a detail
part (this is a set of images with smaller dimensions containing the details of
the original image).
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Input Image
LL1 HL1
LH1 HH1
HL1
LH1 HH1
HL2
HH2 LH2
LL2
HL1
LH1 HH1
HL2
HH2 LH2
LL3 HL3
HH3 LH3
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DNA Encoding
Example:
If we have the sequence T10101101U It will be converted to
TTTGAU
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Our new proposed approach contains four phases: A. DNA Encoding. B. Watermarking Embedding Using DWT. C. Watermark Extraction Using DWT. D. DNA Decoding.
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Proposed watermarking approach based on DNA and Fingerprint Algorithms
The experimental results for the proposed approach have been
evaluated using the following measures:
1- Structural Similarity Index Measure (SSIM):
It designed for quality assessment, it compares local patterns of pixel intensities that
have been normalized for luminance and contrast.
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2- Peak Signal to Noise Ratio (PSNR):
It is a mathematical measure of image quality based on the pixel difference between
two images, it’s an estimate of quality of reconstructed image compared with
original image..
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3- Mean Square Error (MSE):
It is computed by averaging the squared intensity of the original (input)
image and the watermarked (output) image pixels.
Where I and W are the original and the watermarked images having a resolution
of (m * n)
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Histograms of original and watermarked image
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Where: r is the rotation.
t is the translation
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A.GHANY Kareem Kamal, et. al, “A Hybrid Biometric Approach Embedding DNA
Data in Fingerprint Images”, The 3rd IEEE International Conference on Informatics,
Electronics & Vision (ICIEV), Bangladesh, May 23-24, 2014.
This thesis addressed the problem of securing Biometric system, therefore, we
applied a Bio-hash function only then combined it with both the Class Distribution
Preserving (CDP) and the Kekre’s transform to compare the effect of them on the
error rate, i.e. on the performance of the biometric system.
In This thesis we also applied a watermarking technique on a hybrid biometric
system using fingerprint and DNA, and we prove that the watermarking technique
doesn’t affect either the quality of the biometric image or the performance of the
biometric system.
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In the future work we need to apply our approach with new technologies, such as:
1- Apply the Biometric System on Cloud Computing.
2- Apply the Hybrid approach to secure the data on Cloud.
3- Try Biometrics of Animals rather than Human.
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1- A.GHANY Kareem Kamal, A.Moneim Mahmood, Ghali Neveen I., Hassanien
Aboul Ella, Hefny Hesham A., "A Symmetric Bio-Hash Function Based On
Fingerprint Minutiae And Principal Curves Approach", The 3rd International
Conference on Mechanical and Electrical Technology ( ICMET 2011),ASME,
China, vol. 1, 2011.
2- A.GHANY Kareem Kamal, Hefny Hesham A., Hassanien Aboul Ella, Ghali
Neveen I., "A Hybrid approach for biometric template security", The 2012
IEEE/ACM international conference on Advances in Social Network Analysis and
Mining (ASONAM 2012), Turkey, pp. 941-942, 2012.
3- A.GHANY Kareem Kamal, Hefny Hesham A., Hassanien Aboul Ella, Tolba M.
F., "Kekre's transform for protecting fingerprint template", The 13th IEEE
International Conference on Hybrid Intelligent Systems (HIS 2013), pp. 186-191,
2013.
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4- Kareem Kamal A.Ghany, Aboul Ella Hassanien and Gerald Schaefer “Similarity
Measures for Fingerprint Matching” Int. Conference on Image Processing, Computer
Vision, and Pattern Recognition, 21-24 July, 2014, Las Vegas, USA, 2014.
5- Kareem Kamal A.Ghany, Gehad Hassan, Gerald Schaefer, Aboul Ella Hassanien
Md. Atiqur Rahman Ahad, Hesham A. Hefny “A Hybrid Biometric Approach
Embedding DNA Data in Fingerprint Images” The 3rd Intl. Conf. on Informatics,
Electronics & Vision. Dhaka - Bangladesh, 23-24 May, 2014.
6- A.GHANY Kareem Kamal, Hassanien Aboul Ella, Hefny Hesham A., "Machine
Learning in Biometrics: A Review", Book Chapter, Springer, 2014, (Submitted).
7- A.GHANY Kareem Kamal, Sayed Gehad I., Anwar Asmaa S., Ibrahim Fatma M.,
Fathy Ghada D., Hassanien Aboul Ella, Hefny Hesham A., "Securing Maps Data
Using Hybrid Biometrics Watermarking Scheme", International Journal of Coding,
Designs, and Cryptography, Springer, 2014, (Submitted).
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