Presented by: Kushan Ahmadian
Department of Computer Science, University of Calgary
kahmadia@ucalgary.ca
1
Outline
Introduction
Research Contributions
Motivations
Background Research
Neural Network
Dimensionality Reduction
Biometrics
Proposed Methodology
Subspace Clustering
Chaotic Associative Memory
Overall System Architecture
Preliminary Experimental Results
Conclusion and Future Work
2
Research Goal
The purpose of my research is to develop a
novel methodology based on the
subspace
clustering dimension reduction technique
and
chaotic neural network
to improve the
performance
and
circumvention
of multi

modal biometric system.
3
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
My Research Contributions
A novel correlation clustering approach
accounting for the feature relevance and/or
feature correlation problem in multi

modal
biometric system
Design and utilization of
a chaotic associative
neural memory with original noise injection
policy to learn the patterns of biometric
features
Designing and evaluating
the performance of
the system comparing the results to the post

classification (decision level) fusion results
4
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
Motivation
Alleviate problems of current dimensionality
reduction methods
such as “curse of
dimensionality” and “locality” by proposing a
new subspace clustering based dimensionality
reduction
for biometric data.
Reducing the
FAR (False Acceptance Rate)
and
FRR (False Rejection Rate)
by minimizing the
effect of noise, template aging and other errors
using
a novel feature selection method
.
Utilizing a
brain

like associative memory
(chaotic neural network)
for the first time in
biometric
to enhance the ability of pattern

based data retrieval from memory.
5
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
Biometrics
6
Source: http://360biometrics.com/
Biometrics comprises
methods for uniquely
recognizing humans
based upon one or more
intrinsic physical or
behavioral traits
.
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
Multi

modal biometric
7
Matchers Fused
Authors
Level of Fusion
Fusion methodology
Hand
Kumar et al (2003)
Feature, Match score
Feature concatenation/
sum rule
Pulmprint (geometry,
local texture)
You, et al (2004)
Decision
Hierarchical matching
Fingerprint(2
impressions)
Jain and Ross (2002)
Sensor, feature
Mosaicing of
templates
Fingerprint
Wilson et al (2004)
Match score
Sum rule
Face (global and local
features)
Ferrez et al (2005)
Feature level
Feature concatenation
Voice
Cheung et al(2004)
Match score
Zero sum fusion
Face, Iris and
Signature
Gavrilova and
Monwar (2009)
Rank Level
Markov model
Examples of fusion methods.
8
Matchers Fused
Authors
Level of Fusion
Fusion methodology
Hand
Kumar et al (2003)
Feature, Match score
Feature concatenation/
sum rule
Pulmprint (geometry,
local texture)
You, et al (2004)
Decision
Hierarchical matching
Fingerprint(2
impressions)
Jain and Ross (2002)
Sensor, feature
Mosaicing of
templates
Fingerprint
Wilson et al (2004)
Match score
Sum rule
Face (global and local
features)
Ferrez et al (2005)
Feature level
Feature concatenation
Voice
Cheung et al(2004)
Match score
Zero sum fusion
Face, Iris and
Signature
Gavrilova and
Monwar (2009)
Rank Level
Markov model
Feature Space and Dimensionality Reduction
Transform the data in the high

dimensional space to a space
of fewer dimensions.
9
Subspace obtained by PCA and ideal resulted subspace
projected clustering (Han and
Kamber
, 2001)
DBSCAN (Ester et.al. 1996)
Specifications of clustering methods (Achtert and Böhm,
2007).
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
Reducing Dimensionality by Subspace Analysis
The principle for subspace analysis is based on a
generalized description of spherical coordinates.
A point in data space is represented by a
sinusoidal curve in parameter space P.
A point in parameter space corresponds
to a (d − 1)

dimensional hyperplane in data space.
10
Neural network
Chaotic Neural Networks un pattern Rec.(Wang, 2006)
CSA (Chen and Aihara, 1997)
Applications of Optimization (Wang, 1998)
11
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
Traditional System Architecture
12
Traditional multimodal architecture
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
Biometric
Database
Eigenfaces
vectors
PCA

based
dimensionality reduction
User
samples
Yes/No
Learner 1
Learner 1
Learner 1
Aggregation method
Proposed System Architecture
13
Proposed biometric recognition system
Biometric
Database
User samples
Mean faces
Novel representation
of Feature Vector
Train neural
networks
Testing
neural
network
Yes/No
Verified
?
Train
?
N
Y
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
Subspace Clustering Step 1
14
Mean image
for each class
For each person (class) compute the mean image
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
Input Data
Eigenface images
15
•
The eigenvectors are sorted in order of descending eigenvalues and the greatest
eigenvectors are chosen to represent face space.
•
This reduces the dimensionality of the image space, yet maintains a high level
of variance between face images throughout the image subspace.
•
Any face image can then be represented as a vector of coefficients,
corresponding to the
‘
contribution
’
of each eigenface.
Each eigenvector can be displayed as an image and due to the likeness to faces (FERET database)
Subspace Clustering Step 2
16
Number of dimensions: m (number of mean images)
Number of points in the high dimensional space: x*y
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
17
•
Three points p
1
, p
2
, p
3
on a plane (b) Corresponding parameterization functions.
Reducing Dimensionality by Subspace Analysis
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
18
Reducing Dimensionality by Subspace Analysis
Find the clusters within an error range of
ε
.
Use the mean vector as the candidate for
the members of a cluster and create the
new vector space. The number of points of
the new space is:
M << x*y
Next, we try to learn the pattern using a learner (Chaotic
Neural Network)
19
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
Associative Memory
20
•
The neuron signals comprise an output pattern.
•
The neuron signals are initially set equal to
some input pattern.
•
The network converges to the nearest stored
pattern
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
Chaotic Associative Memory
21
Chaotic and period
doubling noise
injection
policies
•
To overcome the
drawback of non

autonomous methods
is their
blind
noise

injecting
strategy
•
Proposing the
adjacency matrix to
evaluate the chances of
a neuron to receive
chaotic noise
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
Fingerprint Neural Based Method
–
Case
Study
22
The general goal is to train the network using the
Delaunay triangulation of minutiae points
.
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
23
DT based Matching

Experimental Results
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
Multimodal Training Phase
24
Analyzing and obtaining the best set of feature
vectors
Data acquisition
Training the chaotic associative
memory with the obtained vectors
User
1
Biometric1
Biometric2
Biometric3
User
2
User
N
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
25
Dimensionality reduction
–
New feature space
Data acquisition
Biometric1
Biometric2
Biometric3
User’s obtained feature
space vector
Feeding the new vector space into
the associative memory
Network
Convergence
(Matching)
Yes/ No
User
Testing
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
Experimental Results
My method: Subspace Clustering (SC) and Chaotic Noise Neural
Network (CNNN)
Compared methods:
Simple

Sum (SS), Min

Score (MIS), Max

Score (MAS), Matcher
Weighting (MW), User Weighting (UW)
Min

Max Score (MM), Zero Score (ZS),
Tanh
(TH), Quadratic
Line Quadric (QLQ), Subspace Clustering (SC)
26
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
Experimental Results
27
EER rate, SC with different fusion techniques
EER rate, CNN with different normalization techniques
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
28
EER rate, Combination of different fusion and normalization techniques
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
Experimental Results
Conclusions
The contributions are :
Introducing method for selecting a proper set
of input features to reduce the dimensionality
of biometric data and consequently
enhancing the performance of the system.
Introducing chaotic associative memories in
biometric system, which have significant
advantages over conventional memories in
terms of capacity of the memory
Implementing and enhancing performance of
the biometric multimodal verification system.
29
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
Future Work
Continuing research on the axis

parallel subspace
clustering.
Comparing to a newly proposed system where the
data analysis is run over the vectors of each biometric
separately. The benefit of such a system would being
more tolerable over the absence of each biometric.
Enhancing the capacity of the associative memories
which is the current drawback of associative based
memories.
Finding a better candidate vector for subspace
clustered data to improve the quality of data
reduction method.
Continuing research on subspace clustering methods
to further decrease FAR and FRR rates
30
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
Project Timeline
31
Phase
Task Name
Start
End
Duration
2008
2009
2010
2011
2012
1
st
Required
course taken. Literature review
on biometrics, feature selection techniques
and associative memories. Problem
statement formulation
.
Sept.
2008
Aug.
2009
12
months
2
nd
Required course taken. Prototype system
development with various associative
neural memory models including chaotic
neural network and send results to different
peer reviewed journals and conferences for
reviewer's feedback.
Sept.
2009
Aug.
2010
12
months
3
rd
Complete system development with
different feature selection policies.
Comments from various reviewers are
highly considered during the complete
system development.
Sept.
2010
Aug.
2011
12
months
4
th
Validation of developed system.
Performing performance analysis of the
proposed system against different
biometric databases. The results will be
communicated through appropriate venues
and consequently modify the system
according to the feedback
Sept.
2011
Jan.
2012
5 months
5
th
The thesis will be prepared as part of the
PhD degree requirements.
Feb.
2012
Jul.
2012
6 months
1
Introduction
2
Background
3
Methodology
4
Experiments
5. Conclusions
Key References
Bohm C,
Kailing
K,
Kriegel
H.P, Kroger P, (2004) Density Connected Clustering with
Local Subspace Preferences,
Proceedings of the Fourth IEEE International Conference on
Data Mining
, p.27

34,
Jain A. K., Ross A., and
Prabhakar
A. (2004) An Introduction to Biometric Recognition.
IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on Image

and Video

Based Biometrics
, 14(1):4
–
20.
Kriegel
H. P,
Kröger
P,
Zimek
Z. (2009) Clustering High Dimensional Data: A Survey on
Subspace Clustering, Pattern

based Clustering, and Correlation Clustering
ACM Transactions on Knowledge Discovery from Data pp.1

58,
Wang L
,
and Shi H (2006) A gradual noisy chaotic neural network for solving the
broadcast scheduling problem in packet radio networks.
IEEE Transactions on neural
networks,
vol
17, no. 4:989

1001
Zhao L. and Yang Y. H., (1999) “Theoretical Analysis of Illumination in PCA

Based Vision
Systems,”
Pattern Recognition
, Vol. 32, No. 4, pp.547

564.
Belhumeur
P. N,
Hespanha
J. P, and
Kriegman
D. J. (1997) Eigenfaces vs.
Fisherfaces
:
recognition using class specific linear projection,
IEEE Trans. Pattern Analysis and
Machine Intelligence
, Vol. 19, No. 7, pp.711

720.
32
Publications
K.Ahmadian
and M. Gavrilova, “Transiently Chaotic Associative Network for
Fingerprint Image Analysis”, Special Issue on Intelligent Computing for
Multimedia Assurance in the
International Journal of Neural Network World,
A. Abraham editor, 2009

2010, 21 pages, in print (accepted in May 2009)
K.
Ahmadian and M. Gavrilova “On

Demand Chaotic Neural Network for
Broadcast Scheduling Problem”, Journal of Supercomputing, 18 pages, Springer
( accepted with minor revisions in May 2010)
.
K. Ahmadian, and M. Gavrilova, “Multi

objective Evolutionary Approach for
Biometric Fusion,” IEEE International Conference on Biometrics and
Kansei
Engineering, pp. 12

17, June 25

28, Poland, 2009.
K. Ahmadian, M. Gavrilova and D.
Taniar
,
“Multi

criteria Optimization in GIS:
Continuous K

Nearest
Neighbor
search in mobile navigation,” ICCSA, pp.574

589, March 2010, Japan.
33
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