Feature Extraction using Sparse SVD for Biometric Fusion in Multimodal Authentication

International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.4, July 2013
DOI : 10.5121/ijnsa.2013.5406 83
Feature Extraction using Sparse SVD for
Biometric Fusion in Multimodal Authentication
Pavan Kumar K1
, P. E. S. N. Krishna Prasad2
, M. V. Ramakrishna3
and
B. D. C. N. Prasad4
1 2, 3 & 4
Prasad V. Potluri Siddhartha Institute of Technology, India
1
pavanpvpsit@gmail.com, 2
surya125@gmail.com, 3
krishna1959@gmail.com and
4
bdcnprasad@gmail.com
ABSTRACT
Token based security (ID Cards) have been used to restrict access to the Secured systems. The purpose of
Biometrics is to identify / verify the correctness of an individual by using certain physiological or
behavioural traits associated with the person. Current biometric systems make use of face, fingerprints,
iris, hand geometry, retina, signature, palm print, voiceprint and so on to establish a person’s identity.
Biometrics is one of the primary key concepts of real application domains such as aadhar card, passport,
pan card, etc. In this paper, we consider face and fingerprint patterns for identification/verification. Using
this data we proposed a novel model for authentication in multimodal biometrics often called Context-
Sensitive Exponent Associative Memory Model (CSEAM). It provides different stages of security for
biometrics fusion patterns. In stage 1, fusion of face and finger patterns using Principal Component
Analysis (PCA), in stage 2 by applying Sparse SVD decomposition to extract the feature patterns from the
fusion data and face pattern and then in stage 3, using CSEAM model, the extracted feature vectors can be
encoded. The final key will be stored in the smart cards as Associative Memory (M), which is often called
Context-Sensitive Associative Memory (CSAM). In CSEAM model, the CSEAM will be computed using
exponential kronecker product for encoding and verification of the chosen samples from the users. The
exponential of matrix can be computed in various ways such as Taylor Series, Pade Approximation and
also using Ordinary Differential Equations (O.D.E.). Among these approaches we considered first two
methods for computing exponential of a feature space. The result analysis of SVD and Sparse SVD for
feature extraction process and also authentication/verification process of the proposed system in terms of
performance measures as Mean square error rates will be presented.
KEYWORDS
Biometrics; Biometric fusion; Face; Fingerprint; Context-Sensitive Exponent Associative Memory Model
(CSEAM); Kronecker Product; Exponential Kronecker Product (eKP); Multimodal Authentication;
Singular Value Decomposition (SVD); Sprase SVD (SSVD);
1. INTRODUCTION
Modelling is the bread and butter for many working researchers and naturally is being applied to
address issues in Biometric security. Many of the speculative queries, researchers and decision-
makers have about security issues in Biometrics can be more practically and efficiently tested
in computer models as opposed to actual physical experiments.
Traditional methods of authentication and identification make use of identification (ID) cards or
personal identification numbers (PINs), but such identifiers can be lost, stolen, or forgotten. In
addition, these models fail to differentiate between an authorized person and an impostor who
fraudulently acquires knowledge or “token” of the authorized person. Security breaches have led
to losses in terms of cost for the sectors like banks and telecommunication systems that depend on
token-based security systems.

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The connectionist models or Artificial Neural Networks (ANN) [2, 18], due to the resemblance of
processing with the form of processing of the human nervous system, they are essential parts of
an emerging field of knowledge known as Computational Intelligence. The use of connectionist
models has provided a solid step forward in solving some of the more complex problems in
Artificial Intelligence (AI), including such areas as machine vision, pattern recognition, biometric
data analysis and recognition. The research in this field has focused on the evaluation of new
neural networks for pattern recognition, training algorithms using real biometric data, and
whether parallel architectures of neural networks can be designed to perform effectively the work
required for complex algorithms for the recognition of biometric patterns.
1.1 Biometrics
Passwords and ID cards used to restrict access to secure systems. When a password or ID card is
available to an unauthorized user, security can be easily breached in these systems. The flaws in
traditional verification methods can be addressed in the biometric authentication systems.
Current biometric systems [8, 10 and 12] make use of face, fingerprints, iris, hand geometry,
retina, signature, palm print, voiceprint and so on to establish a person’s identity. Though there
are limitations in biometric systems, they have some advantages over the conventional security
methods. In this regard the biometric patterns cannot be easily stolen or shared. Biometric
systems also enhance user friendliness by alleviating the need to design and remember passwords.
Biometric systems [7, 10 and 12] can operate in one of two modes 1) the identification mode, in
which the identity of an unknown user is determined, and 2) the verification mode, in which a
claimed identity is either accepted or rejected. Biometric systems are being deployed in various
applications including User logins, ATM PINs, Marts, Adhar cards and any other identity cards.
The successful installation of biometric systems in these applications does not imply that the
biometrics is a not complete solution.
1.2 Multibiometric Systems
By use of multiple biometric modalities (multibiometric systems), the limitations of unimodal
biometric systems can be overcome. The modalities of multibiometric systems are expected to be
more reliable due to the presence of multiple, fairly independent pieces of evidence. These
systems are also able to meet the performance requirements imposed by various applications.
Multibiometric systems [10, 11 and 13] address the problem of non-universality. Due to anti-
spoofing measures available in multibiometric systems, it is difficult for an intruder to spoof the
multiple biometric traits of a user. Multibiometric systems facilitate the challenge-response type
of authentication. A variety of factors to be considered include: 1) the choice and number of
biometric traits, 2) the level in the biometric system that provides the multiple traits to be
integrated, 3) the methodology adopted to integrate the information and 4) the cost versus
matching performance trade-off. A commercial multibiometric system called AdharID integrates
the face, iris, palm and fingerprint of an individual. The information presented by multiple traits
may be consolidated at various levels.
The proposed system can be categorized into three levels, according to the level within which the
authentication/verification is performed. Three possible levels of evaluation: (1) fusion at feature
level, (2) computation level using CSEAM[2], and (3) decision level. Figure 1 illustrates these
different stages of biometric system.
1. Feature Level: the biometric fusion comprises the construction of a new feature vector of
higher order dimensionality. This vector is composed of selection feature elements of

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various feature vectors generated using Sparse SVD(SSVD)[1]/SVD. The new vector is
more discriminative than the individual ones.
2. Computation Level: at this level, fusion matching scores are returned by individual
subsystem and the obtained scores are combined. The normalized scores can be combined
using CSEAM model as neural system so that the fusion of normalized scores leads to a
more accurate overall system.
3. Decision Level: The final decision (in general accept/ reject) is to be performed to make
the decision in this level through performance metrics. Various final decisions of
independent subsystems can be processed by applying a majority voting in order to
increase the accuracy or convenience of the entire system.
Figure 1: Block Diagram of Proposed System
At the feature extraction level, a new feature set is generated from the integration of multiple
modalities of the feature sets. The new feature set is then used in the matching and decision-
making modules of the biometric system. At the computation level, the matching output by
multiple matchers is integrated. At the decision level, the final decisions will be made by the
individual systems consolidated by employing techniques such as majority voting. All these three
levels of process can be applied in three stages of the proposed model.
In most cases, the integration at the feature level usually performs better, but it is not always
feasible for multiple reasons. First, Most of the commercial systems do not provide access to
information at this level. Second, the feature spaces of different biometric traits may not be
compatible. Third, even if the feature sets were compatible, concatenation might result in a
feature vector with a very large dimensionality leading to the “curse of dimensionality” problem.
This problem can be solved using vector logic in the cognitive domain. Here, we proposed a
novel approach for computing the large dimensionality of the feature vector space using
Exponential kronecker Product (eKP) as a part of CSEAM Model, that creates an associative
memory for holding the combined feature patterns of fusion biometric data for
authentication/verification in multimodal authentication process.
1.3 Multi-biometric recognition
Whenever biometric verification systems based on single biometric indicators have to deal with
noisy sensor acquisition, restricted degrees-of-freedom, or non-universality, impractical
performance rates are yielded. These drawbacks are common scenarios when operating biometric
recognition systems that raise the need for multi-biometric recognition or other approaches to
increase the accuracy of recognition. A fusion of multiple biometric indicators shows that
improve the accuracy and reliability of biometric systems.
In this paper we describe a connectionist approach often called CSEAM [2, 4] model to
authenticate the multimodal biometrics, in which the first step is to fusion the face and fingerprint

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biometric patterns through PCA[9] followed by SVD/Sparse SVD[1] decomposition for
extracting the feature spaces as keys for security and then apply the CSEAM model using
exponential kronecker product(eKP)[2, 17] to encode the feature patterns. In the second step the
verification process can be done using the same model.
2. FEATURE EXTRACTION USING SPARSE SVD[1]
The singular value decomposition, or SVD, is a very powerful and useful matrix decomposition,
particularly in the context of data analysis, dimension reducing transformations of images,
satellite data etc, and is the method of choice for solving most linear least–squares problems.
The singular value decomposition (SVD) of X can be written as
X=UDVT
=∑ (1)
The proposed SSVD [1] seeks a low-rank matrix approximation to X as that in (2), but with the
requirement that the vectors uk and vk are sparse, that is, they have many zero entries. We obtain
sparsity by adding sparsity-inducing penalties to the minimization objective in (3). The sparsity
property implies that, the rank-one matrix skukvk
T
, now referred to as an SSVD layer.
Specifically, for the kth
SSVD layer, those rows (or samples) with nonzero uik s are naturally
clustered together, as well as those columns (or variables) with nonzero vjk s. Hence the kth
layer
simultaneously links sets of samples and sets of variables together to reveal some desirable
sample-variable association.
If we consider the first K ≤ r rank-one matrices in the summation in (1), we obtain the following
rank-K approximation to X:
≈ ( )
= ∑ (2)
In fact, X(K)
gives the closest rank-K matrix approximation to X in the sense that X(K)
minimizes
the squared Frobenius norm, i.e.,
( )
= ‖ − ∗‖
∗∈ = {( − ∗)( − ∗) }
∗∈ (3)
The process of SSVD algorithm[1] is computed as :
Algorithm:
1.Apply the standard SVD to X. Let {Sold,Uold,Vold} denote the first SVD triple.
2.Update:
a) Set =sin{(XT
Uold)j(|(XT
Uold)j|-λv , /2)+, j=1…d, where λv is the minimize of
BIC(λv).Let =( ,….., )T
, s=‖ ‖, and vnew= .
b) Set =sin{(X unew)i)}(|(X unew)i|-λu , /2)+, i=1…n, where λu is the minimizer
of BIC(λu).Let =( ,….., )T
, s=‖ ‖, and unew= .
c)Set uold=unew and repeat Steps2(a) and 2(b) until convergence.
3. Set u= uold , v=vnew, s= X vnew at convergence
Where u and v are the degree of sparsity of the sparse singular vectors, λu and λv are the penalty
parameters for the singular vectors u and v.

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Bayesian information criterion (BIC) can be used for the process of selecting the degrees of
sparsity by making use of the connection of SSVD to penalized regression.
BIC( )=
‖ ‖
.
+
( )
f ( ) (4)
BIC( )=
‖ ‖
.
+
( )
f( ) (5)
Where f ( ) [6]is the degree of sparcity of v with as the penalty parameter and is the
Ordinary Least Square(OLS) estimate error. f ( ) [6]is the degree of sparcity of u with as
the penalty parameter and is the Ordinary Least Square(OLS) estimate error.
df( ̂)=∑ ( ̂ , ) / (6)
3. Computation of Matrix Exponential:
The exponential of a matrix [17, 23] A is computed using Taylor series as:
=
!
= + +
∗
2!
+
∗ ∗
3!
+ ⋯
∞
Another approach of computing the exponential of a matrix is first to diagonalize the matrix and
then to compute the exponential of each diagonal element of the matrix. The exponential of a
diagonal matrix can be computed using Pade Approximation [17, 22]. Let A be a diagonal matrix
as:
=
0
0
⋯
0
0
⋮ ⋱ ⋮
0 0 ⋯
Then the exponential of a diagonal matrix is as:
=
0
0
⋯
0
0
⋮ ⋱ ⋮
0 0 ⋯
Also to compute the matrix exponential in various ways using Ordinary differential equation
methods discussed in [17, 23].
3.1 Properties of Matrix Exponential:
The matrix exponential has several properties [17,19 and 23], in which some of the properties are
listed below.
1. =
2. If commute,i.e. AB=BA, then =
3. For any matrix A, eA
is invertible and ( ) =
4. = ( )

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5. =
6. If XY = YX then = = ( )
7. If Y is invertible then =
8. = ( ) , where XT
denotes the transpose of X
3.2 Exponential Kronecker Product (eKP):
The exponential function [19, 20] possesses some specific properties in connection with tensor
operations. Let A and B be the two matrices, then the exponential kronecker product is described
as:
 =

! !
The eKP [19, 20, 21] has nice properties to imply the concept of vector logic theory. The
properties are as:
• ⨂ = ⨂
• ⨂ = ⨁
, which is a special property in the kronecker calculus.
• (  )
= ⨂
In this paper, we have chosen exponential kronecker product as associative memory model [2, 16
and 18] in the connectionist models often called Context-sensitive Exponent Associative Memory
Model (CSEAM).
4. BIOMETRIC FUSION
In this task, the system works as follows: We start with the biometric sample of face and
fingerprint data for training from the user. Once acquired from the user, the fusion of face and
fingerprint image patterns using Principal Component Analysis (PCA) [7, 9, 13] and in parallel
face pattern can be pre-processed to extract the features of face and also from the fusion pattern.
In this case, a feature vector that holds the information of fusion samples are normalized onto
values between [0, 1], transformed into a matrix R (Figure 2), and then compressed into; G  F(I
× J).
Figure 2. Feature vector normalized and transformed into Matrix R
In the next stage, we apply the Sparse SVD[1]/SVD factorization on matrix R, to obtain the
feature space from these two patterns individually with various sizes nxn for recognition as well
as for verification of samples which are collected from the user. Initially we trained the user data
with two samples at the time of registration which is in the form of associative memory M, that
can be stored in the network.

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Once the keys are extracted from the fusion data with various sizes, the generated keys can be
supplied to the connectionist model or ANN model: Context-sensitive Exponent Associative
Memory model (CSEAM) [2,3] for encoding and verification process. In this model, memory is
acted as exponential kronecker product (eKP) [18, 20 and 21], which is powerful concept in the
field of advanced matrices. In the matrix theory, kronecker product can be applied to rectangular
matrices as well as square matrices with different sizes of matrices. Suppose mxn and pxq and
then produce mpxnq resultant matrix. One of the primary advancing concepts in the matrix theory
is to apply exponential to the matrices; this can be done with various approaches, generally, the
experts used to follow the Taylor series and other approaches discussed in [17].
In the proposed model, the feature vectors will be extracted from fusion and face patterns that can
be passed to CSAM model for computing exponential kronecker product and the result will be
stored in the smart cards as Associative Memory (CSAM) for verification.
5. CONTEXT-SENSITIVE EXPONENT ASSOCIATIVE MEMORY MODEL
(CSEAM)
In the cognitive domain, information stored in the brain is extensively used by the cognitive
functions and the task of searching for the relevant information for solving a problem is a very
complex. Human cognition uses biological search engines. Cognitive functions need to
understand the way these search engines work. The approach is to study multi modular network
models that are able to solve particular problems involving information searching. The building
blocks of these multi modular networks are the context-dependent memory models. These models
work by associating an output to the Kronecker product of an input and a context. Input, context
and output are cognitive variables in the form of vectors.
Vector logic [2, 3] is a mathematical model of logic in which the truth values are mapped on to
elements of a vector space. The binary logical functions are performed by rectangular matrices
operating on the Kronecker product of their vectorial arguments. The binary operators acting on
vectors representing ambiguous (fuzzy) truth values generate many-valued logics. [3 and 16]
showed that, within the formalism of vector logic, it becomes possible to obtain truth-functional
definitions of the modalities “possibility” and “necessity”. These definitions are based on the
matrix operators that represent disjunction and conjunction respectively, and each modality
emerges by means of an iterative process. The mathematical representations of logic have opened
illuminating perspectives for the understanding of the logical constructions created by the
humans.
Memory plays a major role in Artificial Neural Networks [4, 15and 16]. Without memory, Neural
Network cannot be learned by itself. In neural networks, one of the primary concepts of memory
is associative neural memories. It accesses the memory by its contents, not by where it is stored in
the neural pathways of the brain. Memory capacity and Content-addressability are traditional
measures of associative memory performance. The maximum number of associated pattern pairs
that can be stored and correctly retrieved is often referred to as Memory Capacity. The content-
addressability is the ability of the neural network to retrieve the correct stored patterns.
Associative neural memories are concerned with associative learning and retrieval of information
in the form of vector patterns in the networks.
Associative memories [2, 4] can be implemented either in feed forward or recurrent neural
networks. Such networks are used to associate one set of patterns with another set of patterns as
input and output vectors. The purpose of an associative memory is to produce the associated
output vector whenever one of the input vectors is applied to the neural network. The input vector

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may be applied to the network either as input or as initial state and the output vector is observed
at the outputs of some neurons constituting the network. Context-sensitive associative memories
are models that allow the retrieval of different vectorial responses given the same vectorial
stimulus, depending on the context presented to the memory. The contextualization is obtained by
the Kronecker product between two vectorial entries to the associative memory: the key stimulus
and the context. These memories are able to display a wide variety of behaviours that range from
all the basic operations of the logical calculus (including fuzzy logics) to the selective extraction
of features from complex vectorial patterns.
By providing a rich knowledge representation capable of representing highly complex knowledge
that supports the features required for the context sensitive search, the experience store provides a
task independent basis for context-sensitive search.
Context-Sensitive Exponent Associative Memory model (CSEAM) [2, 3 and 16] is a novel model
for information access, which is general, scalable, operates in parallel with the reasoning, controls
the cost of the retrieval and exploits contextual information that improves the performance. A
CSEAM is built upon an associative based retrieval manager that can be implemented with vector
logic, a context-sensitive search process and a content addressable store.
Here, CSEAM model [2, 3 and 16] is described using the concept of vector logic, which is one of
the prime logic mechanisms to store and retrieve the associated patterns using the Context-
sensitive search model with the support of content addressability. The associative memory model
accesses the memory patterns as cue by its contents and not exactly where it is stored in the neural
pathways of the model. The performance of associative memory is its memory capacity and
content addressability.
Associative memories are connected to associative learning and retrieval of vector patterns in the
semantic nets. Associative nets are used to associate one set of patterns with another set of
patterns and produce output patterns. In CSEAM model, the associative memory is represented as
an exponential kronecker product (eKP) that associates two sets of input patterns to frame
memory model often called exponent associative memory model (M). Mathematically, this model
is represented as:
=  =

! !
The two input patterns A and B are represented as vectors or matrices using vector logic, then
apply exponential to such vector patterns and then apply kronecker product these exponential
matrices. Finally, this model gives an associative memory which is of exponential. We suggested
the name for this model as exponent associative memory with the use of kronecker product based
on the context-sensitive search and content addressability. It is conceived that the model is often
called as context-sensitive Associative memory (CSEAM) model
6. PROPOSED MODEL
In this paper, we proposed this model for recognition and authentication of biometric data [5, 7
and 11]. Two kinds of biometrics such as face and fingerprint are considered as inputs of this
network for making an association between these two patterns by applying the chosen model to
create a memory model (M). The proposed model is presented as in Figure 1 that represents the
recognition of the biometrics to train and test the network using CSEAM model. The resultant
memory is stored in the trained network. 2(b) represents the authentication or verification of the
system based on the user provided samples, these samples are supplied as inputs to the same

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model to create memory MT
and then the created memory MT
is compared with the existing
memory M in the trained network. The Memory model MT
is computed as:
= ⨂
Where A and B are the keys, which are generated from the user for verification. If matched, the
provided samples are verified; otherwise authentication failed.
The processing of a proposed system is described in different stages. In stage 1, the system
acquires the biometric patterns face and fingerprint either for registration or for verification. Once
acquired, those two patterns can be pre-processed and then extracted features as vectors, then
these features will be represented in matrix form. After acquiring the features, the keys are
generated by applying the SVD/SSVD factorization methods. Then, the generated keys are
transformed to the proposed model CSEAM for registration to represent as Associative Memory
M. The resultant memory is stored in the trained network for verification whenever the user wants
to verify the registered data for his/her usage of the system. Similarly, the same process is
continued in the verification mode, there the computed memory MT
is compared with the memory
M that is available in the trained network. The difference will be computed through some
performance metrics such as mean square error (MSE). In this model, we fix the threshold of
MSE for SVD based model, d= 0.001 and for SSVD, d=0.01 based on the normalized errors for
both proposed models, which is minimum of the tested samples. Based on the threshold errors,
the user provided data will be verified by using the proposed model
7. EXPERIMENTAL ANALYSIS
In the experiments conducted on this model, we test the verification performance on the standard
databases from [25, 26 and 27], and also on the realistic data collected through webcam for
training and testing. In the evaluation of verification performance, we computed the Mean Square
Error (MSE) [24] based on the error which is the difference between training and testing
memories M and MT
respectively. The error is computed as: = − , then the MSE will be
computed with the following equation.
=
1
.
The experimental results on the chosen databases [25,26,27] are given in tables 1, 2, 3 and 4 with
different sizes such as 8x8,16x16,....,64x64 on similar and dissimilar face and fingerprint patterns.
7.1 False Match Rate (FMR) and False Non-Match Rate (FNMR)
To characterize a biometric system, FNMR and FMR parameters have been considered for
authentication/verification process. Supposing that there are no errors in the acquisition, the
FAR/FMR and FRR/FNMR pairs are equivalent.
False match rate (FMR)[24] is the probability of the system matching incorrectly the input data to
a non-matching template in the database, i.e. the percentage of imposters incorrectly matched to a
valid user’s biometric. It measures the percent of invalid inputs which are incorrectly accepted.
FMR is obtained by matching face and fingerprint of different people. The FMR parameter is
computed as the percentage of matching whose error value is equal or less than the threshold d:
MSE ≤ d, where the threshold d is the set of possible values of the global error.

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False non-match rate (FNMR) [24] is the probability of the system not matching the input data to
a matching template in the database, i.e. the percentage of incorrectly rejected valid users. It
measures the percent of valid inputs which are incorrectly rejected. FNMR is obtained by
matching biometric data of the same people. The FNMR is computed as the percentage of
matching whose error is greater than the threshold d: MSE>d.
MSE
S.NO Key Size Similar Dissimilar
1 8x8 1.982 1.2935
2 16x16 0.0211 0.2257
3 24x24 2.42E-02 0.1153
4 32x32 2.86E-02 0.0863
5 40x40 1.66E-02 0.0642
6 48x48 1.26E-02 0.0543
7 56x56 1.07E-02 0.0447
8 64x64 8.50E-03 0.0393
Table 1: MSE of various key sizes for Fusion based CSEAM with SSVD using Taylor Series
MSE
1 8x8 1.982 1.2935
2 16x16 0.0211 0.2257
3 24x24 2.42E-02 0.1153
4 32x32 2.86E-02 0.0863
5 40x40 1.66E-02 0.0642
6 48x48 1.26E-02 0.0543
7 56x56 1.07E-02 0.0447
8 64x64 8.50E-03 0.0393
Table 2: MSE of various key sizes for Fusion based CSEAM with SSVD using Pade Approximation
Table 3: MSE of various key sizes for Fusion based CSEAM with SVD using Taylor Series
MSE
1 8x8 0.0012 0.0232
2 16x16 0.0025 0.0034
S.NO Key Size
MSE
Similar Dissimilar
1 8x8 0.0162 0.0488
2 16x16 0.0011 0.0074
3 24x24 4.50E-04 0.0067
4 32x32 4.44E-04 0.0033
5 40x40 3.92E-04 0.0027
6 48x48 2.71E-04 0.0018
7 56x56 2.08E-04 0.0015
8 64x64 1.82E-04 0.0013

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3 24x24 1.40E-03 0.003
4 32x32 6.81E-04 0.0015
5 40x40 4.64E-04 0.0012
6 48x48 3.71E-04 8.06E-04
7 56x56 3.24E-04 6.72E-04
8 64x64 2.78E-04 5.83E-04
Table 4: MSE of various key sizes for Fusion based CSEAM with SVD using Pade Approximation
By the observation of the experimental results, The proposed model works with Sparse SVD and
SVD algorithms for feature/ key extraction process for authentication and also the Computation
model (CSEAM) works with two approaches 1) Taylor Series and 2) Pade Approximation. From
these combinations, it is notified that the Sparse SVD based approach for both computation
models (CSEAM using Taylor series/Pade Approximation) gets the same results and the key sizes
8x8 and 16x16, have been encountered in rejecting rate when provided similar biometric data
patterns (FMR). Whereas SVD based computation models (CSEAM using Taylor series/Pade
Approximation) differs the results for similar and dissimilar based data. The key sizes 8x8 and
16x16, have been encountered in rejecting rate when provided similar biometric data patterns
(FMR) for both computation models, but in process of dissimilar data patterns the key sizes
48x48 onwards have been accepted when we are using Pade approximation based CSEAM model
with SVD. The results are discussed in the table 1,2,3 and 4 for all approaches.
7. CONCLUSIONS
In this paper, we proposed a novel model in the cognitive logic often referred as CSEAM model
(Taylor / Pade Approximation) through Sparse SVD/SVD based feature extraction for
authentication/ verification of the biometrics data in multimodal authentication. The Sparse SVD
based CSEAM model using Taylor series gives better results from the chosen databases when
compared with other approaches and provides more complex security in terms of time and space,
which uses exponential kronecker product in the vector logic that can be computed using Taylor
series. From the observation of experimental results, the key sizes should be more than 16x16,
since while extracting the feature and applying the PCA, some of the features might be lost. In
such scenarios, the biometric data will be refused by the model. For the rest of the cases the
proposed model gives better results.
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