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23KRISHNESWARI & ARUMUGAM : AN IMPROVED GENETIC OPTIMIZED NEURAL NETWORK Journal of Scientific & Industrial Research
Vol. 72, January 2013, pp. 23-30
*Author for correspondence
E-mail: krishneswari@gmail.com
An improved Genetic Optimized Neural Network for Multimodal
Biometrics
K.Krishneswari1 and S.Arumugam2
1Tamilnadu College of Engineering, Coimbatore, Tamilnadu, India*2 Nandha Educational Institutions, Erode, Tamilnadu, India
Received:28 May 2012 ; revised:14 September2012 ; accepted:05 November 2012
In this paper, a novel classification technique for multimodal biometric system based on fingerprint and palmprint is
proposed. The problems faced in unimodal biometric system such as noisy data, intra class variations, restricted degrees of
freedom, non-universality, spoof attacks, and unacceptable error rates are overcome in multimodal biometric system by integrating
the evidence presented by multiple traits. It is proposed to fuse the features of the fingerprint with palmprint images. Features are
extracted using Gabor filter and Discrete Cosine Transform (DCT). The extracted feature vectors were classified using an
improved Partial Recurrent Neural Network with genetic optimization. The proposed Momentum Optimized Genetic Partial
Recurrent Neural Network (MOG-PRNN) was evaluated using a publicly available dataset and features obtained from live
dataset. The experimental results obtained show an average classification accuracy of 98.6% with different datasets.
Keywords: Unimodal Biometric System, Multimodal Biometric system, Fingerprint, Palmprint, Genetic Algorithm, Neural
Network.
Introduction
Biometrics refers to the science of measuring andanalyzing biological data. It is used for recognizing an
individual based on the physiological or behavioral traitsfor determining an individual’s identity. Fingerprints, hand
geometry, iris, retina, face, hand vein, facial thermo gram,
signature, voice, are used for determining or authenticating an identity in biometric technology1 .
Biometric authentication systems are widely used in publicand corporate security system. Biometric system is
basically a pattern recognition system which acquires
biometric data from the individual; features are extractedand compared with the database for identification or
authentication.In identification method, the system recognizes an
individual by matching the biometric data in the database.
During authentication, the system confirms theindividual’s identity by comparing it with biometric
template stored in the database. Biometric authenticationsystems are preferred to traditional authentication
systems as it is not password or key dependent. Unimodal
biometric systems are the most commonly used systems.They use one single source of biometric information like
fingerprint or face for authentication. The problems faced
by such systems are:• Noise in the data; a scar on the finger, or cold
affecting the voice, or poor lighting for facerecognition
• Intra-class variations caused by user due to
incorrect interaction with sensor • Inter-class similarities in the feature space
• Spoof attacks
Some of the limitations of unimodal biometric systemscan be overcome by the use of multiple biometric data
for establishing identity2. Such systems, known as
multimodal biometric systems, are more dependable asmultiple, independent biometric data of the individual are
used for identification3. In this paper, a multimodal biometric system using fingerprint and palmprint features
for classification is proposed. Fingerprint is the most
commonly used human trait due to the uniqueness andthe well formed pattern of ridges, furrows and whorls.
Palmprint is a reliable biometric technique as it containsMany features like principle lines, ridges, minutiae points,
singular points and texture. Generally palmprint is
expected to have more distinctive features than afingerprint4. In this paper, features are extracted using
Gabor filter and Discrete Cosine Transform (DCT). Toclassify the features the Momentum Optimized Genetic
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24 J SCI IND RES VOL 72 JANUARY 2013
Partial Recurrent Neural Network (MOG-PRNN) is
proposed.
Related Work
There are many work in literature related to palmprint
recognition systems. Satoshi Iitsuka, et al., 5 presented
a palmprint recognition algorithm using Principal
Component Analysis (PCA) of phase information in two-
dimensional Discrete Fourier Transforms (DFTs) of
palmprint images. Experiments demonstrated that the
proposed method greatly reduced computational cost
without sacrificing recognition performance. Compared
to earlier work, the Phase-Only Correlation (POC)
proposed an image matching technique which used phase
components in 2D DFTs of given images. The results
showed that limiting the frequency bandwidth and
averaging phase components played a dominant role in
improving recognition performance.
Anil K. Jain and MeltemDemirkus6 proposed a
palmprint matching system having many palmar features
including friction ridges, minutiae, flexion creases and
palmar texture. The proposed system could match a full
or latent input image to a full palmprint database using
Gabor filters and Active Contour Model. Experimental
results showed a matching accuracy of 98.9% at an FAR
of 0.01% for full-to-full palmprint matching. In partial-
to-full palmprint matching, rank-1 retrieval accuracies
of 95.6% and 82% were achieved for synthetic latent
and pseudo-latent palmprint databases, respectively. In7,
discrete cosine coefficients, invariant local binary
patterns, Gabor filters and ensembles of matchers were
employed for palm image authentication.
Altun8 proposed a palmprint verification technique
using Gabor filter for feature extraction and Genetic
algorithm for feature selection. A novel Neural Network
combining back propagation techniques and Particle
Swarm Optimization (PSO) was proposed. Recognition
rates of 96% were obtained. Lin, et al., 9 used two finger-
webs as datum points for defining the region of interest
in palmprints. Principal Palmprint features inside the
region of interest was extracted using hierarchical
decomposition mechanism. The proposed mechanism
applied directional and multi-resolution decompositions
showed effective results for verification.
Multimodal biometric systems consolidated theevidence presented by multiple biometric sources and
typically better recognition performance compared to a
system based on a single biometric modality. In
multimodal authentication, various combinations of traitswere proposed in literature to improve the system
performance. Some of them used face and palmprint for identification10, 11, 12, palmprint and hand vein 13, palmprint
and finger geometry14, fingerprint and palmprint15 or
palmprint and iris17. Snelick, et al.,10examined the performance of multimodal biometric authentication
systems using state-of-the-art Commercial Off-the-Shelf (COTS) fingerprint and face biometric systems on 1000
individuals. Most studies of multimodal biometrics were
limited to low accuracy non-COTS systems and withlimited number of biometric template. The study revealed
that while COTS based multimodal fingerprint/face biometric systems could perform better than Unimodal
COTS systems. The performance improvement was
insignificant and this was on expected lines as COTS
systems left little room for improvement. Also if relative performance gains were considered, 1 per cent equalerror rate (EER) improvement would mean a fifty per
cent reduction of false accept/false reject numbers when
the system is accurate. Nagesh, et al.11, integrated the palmprint and face
features to increase the robustness of authenticationsystem. The final authentication was made by fusion at
matching score level architecture where both the features
of face and palmprint vectors created independently fromquery measures and compared to the enrolment template
in the database. The experimental results showed that
the proposed multimodal system on a data set containing720 pairs of images from 120 subjects, performed well.
It was showed that multimodal system performed better in comparison to the Unimodal biometrics with an
accuracy of more than 98%.Pengfei Yu, et al ., 14 combined palm print feature
and finger geometry feature based on Canonical
Correlation Analysis (CCA) for fusing of individualfeature to combined feature to denote the identity of a
person in multimodal biometric system. The proposed
method had the added advantage of decreasing the
dimension of the fusion feature. Chin, et al .,
15
proposeda multimodal biometrics system that combined fingerprint
and palmprint features. The prints are preprocessed to
enhance the quality and 2D Gabor filters was used to
extract features. The features extracted from palmprintand fingerprint was concatenated into single vector
combining unique characteristics for enabling
discrimination against imposters. Experiments
demonstrated that equal error rate (EER) as low as
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25KRISHNESWARI & ARUMUGAM : AN IMPROVED GENETIC OPTIMIZED NEURAL NETWORK
0.91% were obtained using the combined biometric
features. Xiao-Yuan, et al., 16 proposed a pixel level biometric fusion approach to solve small sample
recognition problem. The face and palmprint images weretransformed using Gabor and combined at pixel level. A
classifier, KDCV RBF, was proposed for classifying the
fused images. Kala, et al ., 22 presented a way to handledimensionality by dividing attributes to various modules
of the modular neural network. The proposed methodlimits the dimensionality without much loss of information
and also helps the system to train faster. A mechanism
for weighing various modules is also suggested to helpimprove the results by removing the bad modules from
affecting decision.The proposed method was applied to a multimodal
system based on face and speech and experimental
results gave good recognition of 97.5%. New fusionfunctions were parameterized with genetic algorithm and
built using genetic programming in 24. The proposedmethod was used for building a low cost multimodal
system based on keystroke and 2D face recognition.Experiments show that the results are improved byobtaining a ERR of 2.22%. The advantage of Genetic
programming is that the complex and adaptive fusionfunctions can be well defined, thus outperforms the other
traditional methods in biometrics. From literature it is seen
that various techniques using palmprint based featureextraction have been proposed with emphasis on texture
and line based feature extraction. Various techniques havealso been proposed for identifying points to extract the
Region of Interest (ROI). In the case of multimodal
biometrics various combinations have been proposed. Itis seen that reliability of the system generally improves
with multimodal biometrics in terms of consistent resultscompared to Unimodal biometrics. However the
classification accuracy is within the same range.
Materials and Methods
In this paper it is proposed to fuse palm print imagewith finger print image and extract features using Gabor
filter for texture, energy coefficients in the frequencydomain using Discrete Cosine Transform. Palm print of
20 users with 10 samples each was obtained from HongKong Polytechnic University palm print Database. 20
fingerprints for fusion with selected palm print database
were obtained from FVC2002 DB4B dataset. Sample palmprint image and fingerprint image are shown in
(Fig. 1a and 1b).
Image fusion is the process of combining two or more images into a single image. In this paper Bi
orthogonal wavelet decomposition is done on the imagesto be fused with the wavelet decomposition of the two
original images are merged. During fusion the minimumapproximation of both the images are used. Since image
fusion requires both the images to be of the same size,
the images are resized before fusion. Sample image after fusion is shown in (Fig. 1c).
Gabor filters are useful tools in image processing, asit has optimal localization properties in both spatial and
frequency domain23. The Gabor function is a harmonic
oscillator present within a Gaussian envelope andcomposed of sinusoidal plane wave. A 2-D Gabor filter
over the image (x, y) can be defined as:
( )( ) ( )
( ) ( )( )( )
2 2
0 0
2 2
0 0 0 0
, exp2 2
x exp 2
x y
x x y yG x y
i u x x v y y
σ σ
π
− − = − −
− − + −...(1)
Where
( )( )
( )
0 0
0 0 0
2 2
0 0 0
0 0 0 0
, specify location in image,, specify modulation that has spatial frequency
is orientation, arctan /
and are standard deviations x y
x yu v
u v
v u
ω
ω
θ θ
σ σ
= +
=
Discrete Cosine Transform (DCT) is a Fourier-
related transform, which is real valued. It can be
(a) (b) (c)
Fig.1—(a) Sample palm image (b) sample finger image (c) fused image
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26 J SCI IND RES VOL 72 JANUARY 2013
implemented using the Discrete Fourier Transform(DFT)18. The DCT calculates a truncated Chebyshev
series. The DCT expresses the data in terms of sum of cosine functions. The most common type of DCT used
operates on a real sequence of length N to produce
coefficients Ck, as follows:
...(2)
And
...(3)
Where
The DCT has strong energy compaction property.
Fast computation techniques can be applied for featureextraction, thus widely used in pattern recognition. DCThas been successfully used in face recognition19 instead
of Karhunen-Loeve transform (KLT), as DCT arecomputationally less intensive. Neural Networks is a fieldof Artificial Intelligence (AI) inspired from the working
of human brain. It can find data structures and algorithmsfor learning and classification of data. Using computer with conventional programming, it is difficult to perform
tasks such as pattern recognition. But neural networkslearn by examples, and after learning process can classifyinputs accordingly.
Recurrent neural networks (RNN) are dynamicneural network which uses not only the current inputs
but also the previous operations of the network. In RNN,neuron outputs are fed back into the network as additional
inputs with time delay elements 20. In simple, RNN have
one-to-one recurrent connections where neuron outputs
are fed back into the network as the input of one neuron
and not to all neurons. The recurrent connections have a
time-delay and the rest of the forward connections are
instantaneous. Context layers are layers that use
recurrent connections in its computations. Simple RNN
generally use fixed recurrent weights. Elman and Jordannetworks are simple recurrent networks with fixed
recurrent weights and fixed recurrent connections. If
the feedback is in only one of the layer then it is referred
to as Semi Partial Recurrent Neural network (SPRNN).
The recurrent networks are dynamic in nature as the
feedback loops use unit delay elements. PRNN hasfeedback in any one of the layers only. PRNNs are easier
to use than the RNNs. Time is implicitly represented inPRNN. Simple PRNN consists of two-layer network
with feedback in the hidden layer as shown in (Fig. 2).
The output of the hidden layer at time t is fed back asadditional inputs at time t+1, thus the PRNN works in
discrete time steps. The proposed PRNN has laguarrefunction in the input layer and a tanh function in the hidden
layer. The tanh function being asymmetric helps to train
faster.The output of PRNN when a input vector x is
propagated through a weight layer V , and the previousstate activation due to recurrent weight layer U ,
Fig. 2—A simple Partial Recurrent Neural network
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27KRISHNESWARI & ARUMUGAM : AN IMPROVED GENETIC OPTIMIZED NEURAL NETWORK
… (4)
...(5)
Where n is the number of inputs, is bias, f outputfunction, m is number of state nodes, and i , j / h , k
denotes the input, hidden and output nodes respectively.
The output of the network with output weights W,
… (6)
In opposition to multi-layer feed-forward networks,
the output of a hidden unit on recurrent networks is sent
back in order to be used as an input on the next step.
Beside the input, hidden and output layer, a set of “context
units” is added in the input layer here. There are
connections from hidden layer to these context units with
fixed weight. At each time step, the input is propagated
in a standard feed-forward fashion, and then a learning
rule (usually back-propagation) is applied. The back
connections result in the context units always maintaining
a copy of the previous values of the hidden units (since
they propagate over the connections before the learning
rule is applied). The right selection of these connection
values is very important on training success of these
networks. Similarly the selection of learning rule andlearning rate determines the effectives of the neural
network. Learning rate is used to adjust the old weights
such that the divergence is not very high when the old
weight is changed. If the learning rate is high then the
neural network may learn quickly. The neural network
may take a long time for learning with a smaller learning
rate. This is partially overcome by using a revised process
for weight adjustment as shown
...(7)
Where
is the current weight computed for connection
between neuron i and neuron j,
are the previous and next to previous
weight , M is the momentum.
It can be seen that momentum allows the weights to
persist for a number of cycles for adjustment. Larger the value of momentum, the higher the persistence of
previous weights for computing the current weights.Momentum helps in improving the learning rate by
smoothing out unusual conditions during the training
phase. However these techniques suffer from beingtrapped in local minima and may ultimately end up having
a very high computational time. So in order to eliminatethe limitations and make the training more effective, one
of the best approaches is to use heuristic search
algorithms which perceive the weights of the network as parameters.
The GA is a global search procedure that searchesfrom one population of points to another 21. As the
algorithm continuously samples the parameter space, the
search is directed toward the area of the best solution so
far. This algorithm has been shown to performexceedingly well in obtaining global solutions for difficultnon-linear functions. A formal description of the algorithm
is provided in Goldberg21. Basically, an objective function,
such as minimization of the sum of squared errors or sum of absolute errors, is chosen for optimizing the
network.The objective functions need not be differentiable or
even continuous. Using the chosen objective function,
each candidate point out the initial population of randomlychosen starting points and is used to evaluate the
objective function. These values are then used in
assigning probabilities for each of the points in the population. For minimization, as in the case of sum of
squared errors, the highest probability is assigned to the point with the lowest objective function value. Once all
points have been assigned a probability, a new populationof points is drawn from the original population with
replacement. The points are chosen randomly with the
probability of selection equal to its assigned probabilityvalue.
Thus, those points generating the lowest sum of squared errors are the most likely to be represented in
the new population. The points comprising this new
population are then randomly paired for the crossover
operation. Each point is a vector (string) of n parameters(weights). A position along the vectors is randomly
selected for each pair of points and the preceding
parameters are switched between the two points. This
crossover operation results in each new point having parameters from both parent points. Finally, each weight
has a small probability of being replaced with a value
(1-M) Learning rate error
input M ( )
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28 J SCI IND RES VOL 72 JANUARY 2013
randomly chosen from the parameter space. Thisoperation is referred to as mutation. Mutation enhancesthe GA by intermittently injecting a random point in order to better search the entire parameter space.
This allows the GA to possibly escape from localoptima if the new point generated is a better solution
than has previously been found, thus providing a morerobust solution. This resulting set of points now becomesthe new population, and the process repeats untilconvergence. Since this method simultaneously searchesin many directions, the probability of finding a globaloptimum greatly increases. As the GA progresses throughgenerations, the parameters most favorable for optimizing the objective function will reproduce and thrivein future generations, while poorly performing parametersdie out, as in “survival of the fittest”. Research using theGA for optimization has demonstrated its strong potential
for obtaining globally optimal solutions.In standard Genetic algorithm, population of n
individuals, with fitness f , the selection function is a probability function given by:
… (8)
on population {x1,…..,x
n}
Each of the fused images in the spatial domain wasconverted to Frequency domain using Eqs. (2) & (3),
and texture features were extracted using Eq. (1) with 6orientations. Features relevant to the class were extractedfrom the extracted features using Information Gain (IG).
Let ‘A’ be the set of all attributes and Tx the set of all training examples, value(x,a) with xÎTx defines thevalue of a specific example x for attribute xÎA, H specifiesthe entropy and | x | is the number of elements in the setx. The information gain for an attribute a∈A is definedas follows:
H …(9)
Result and Discussion
The architecture of the proposed classificationmechanism is shown in (Table I). Two scenarios wereconsidered. In the first scenario the momentum wasvaried between 0.5 to 0.9 and the Mean Squared Error (MSE) measured. GA was used for finding the optimalmomentum and again the fitness function measured inthe second scenario.
The proposed neural network was able to obtain aclassification accuracy of 98.18 %. The MSE is shown
Table I—Architecture of the proposed mechanism
Number of neurons in input layer 34 Number of neurons in hidden layer 10Activation function SigmoidMomentum lower bound 0.3Momentum upper bound 0.9
Number of iterations 500 population size for GA 20Maximum number of generations 10Encoder mechanism BinaryCross over type Two point with probability of 0.8Mutation Uniform with probability of 0.05
Table II-Mean Squared Error with STD. deviation
Momentum Min. MSE + 1 S D - 1 S DTraining
0.5 0.152 0.173 0.1320.6 0.196 0.262 0.1290.7 0.160 0.177 0.1440.8 0.162 0.168 0.157
0.9 0.157 0.167 0.147
Fig. 3—Momentum vs Mean squared error plot.
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29KRISHNESWARI & ARUMUGAM : AN IMPROVED GENETIC OPTIMIZED NEURAL NETWORK
in (Table II) and plotted in (Fig. 3).The average trainingMSE is shown in (Fig. 4). From (Fig. 4) it can be seen
with momentum = 0.5 the MSE is at the lowest and the
training occurs within 250 epochs. This reduces the timeof training considerably.
The comparison between the MSE with and withoutGenetic Optimization of the momentum is shown in
(Table III). From (Table III) it can be seen that duringthe training phase Genetic optimization improves the MSE
by more than 15% which is significant for biometric
based solutions.Experiments were conducted using our palm and
fingerprint database. Since the Hong Kong Poly Udatabase consists of images captured using pegged
techniques, it is proposed to collect fingerprint and palmprints using pegless technique and measure theaccuracy of the proposed system .The palm prints were
captured using digital camera which has a 2 Megapixel.The palm images were captured from 71 individuals, 8
samples each, to form the database. The fingerprints
were captured using digital fingerprint biometric reader.The obtained images were filtered for noise removal using
median filter and the region of interest extracted. The
process for feature extraction used in the previous dataset
was again used in this work . The average classificationaccuracy obtained is 97.83 %. The average MSE during
training for 5 runs is shown in (Fig. 5).The (Fig. 6) shows the individual MSE for each run.
It is seen that due to an absolute increase in the MSE
during the third run contributes to the overall higher MSE.This can be attributed to the sub optimal solution for the
weight adjustment. It can be seen that even with noisyimages captured through a camera under pegless
conditions is able to provide accurate results compared
to data acquired from a controlled environment.
ConclusionIn this paper it was proposed to investigate the
efficacy of Neural Network for multimodal biometrics.
A novel neural network was proposed with genetic
Fig.4—The training MSE vs the number of iterations.
Table III—MSE with and without Genetic Optimization
Minimum Final
MSE - without GA 0.1307 0.1593
optimization
MSE - with GA 0.111333 0.111333
optimization
Fig.5—The average MSE from 5 runs
Fig. 6—The MSE vs 200 epochs
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30 J SCI IND RES VOL 72 JANUARY 2013
optimization. Publicly available dataset and data obtained
from live fingerprint and palmprint were used in this
research. Gabor features and DCT coefficients were
extracted from fused multimodal images. Using
information gain the top 35 features were extracted for
training and testing the proposed classifier. The proposed
PRNN with Genetic optimization decreased the MSE
by 15%. The proposed method improves the classification
accuracy by a factor of 1.83 compared to 8 when using
the images obtained from pegless method and by a factor
of 2.18 using the Hong Kong Poly U dataset. It is seen
that multimodal technique can be more efficient for
recognition compared to unimodal techniques used in 8.
References1 Jain A K, Ross A & Prabhakar S, An introduction to biometric
recognition, IEEE Trans. on Circuits and Systems for Video
Technol , 14 (2004) 4–20.2 Ross A & Jain A K, Information fusion in biometrics, Pattern
Recognit Lett , 24 (2003) 2115–2125.
3 Kuncheva L I, Whitaker C J, Shipp C A & Duin R P W, Is
independence good for combining classifiers?, in Proc. of Int’l
Conf on Pattern Recognition (ICPR), vol 2 (Barcelona, Spain)
2000, 168–171.
4 Zhang D, Palmprint Authentication (Kluwer Academic
Publication) 2004.
5 Iitsuka S, Miyazawa K & Aoki T, A Palmprint Recognition
Algorithm Using Principal Component Analysis Of Phase
Information, in ( ICIP) 2009, 1973-1976.
6 Jain A K & Feng J J, Latent Palmprint Matching, IEEE Transact
on Pattern Anal and Machine Intell , 31 (2009) 1032 - 1047.
7 Nanni L & Lumini A, Ensemble of multiple palmprintrepresentation, Expert Systems with Applicat , 36 (2009)
4485–4490
8 AdemAlpaslanAltun, A combination of Genetic Algorithm,
Particle Swarm Optimization and Neural Network for palmprint
recognition, Neural Comput Applicat , (2012).
9 Chih-Lung Lin, Thomas C & Chuang ,Kuo-Chin Fan, Palmprint
verification using hierarchical decomposition, Pa ttern
Recognit , , 38 (2005) 2639-2653.
10 Robert Snelick, UmutUludag, Alan Mink, Michael Indovina, &
Anil Jain, Large-Scale Evaluation of multimodal Biometric
Authentication Using State-of-the-Art Systems, IEEE Transact
on Pattern anal and Machine Intelli, 27(2005).
11 Nagesh Kumar M, Mahesh P K & ShanmukhaSwamy M N, An
Efficient Secure Multimodal Biometric Fusion Using Palmprint
and Face Image, Inter J Computer Sci Issues, 2 (2009).
12 LinlinShen, Zhen Ji, Yuwen Li & Li Bai, Coding Gabor Features
for Multi-modal Biometrics, Pattern Recognit (CCPR), (2010)
1-4.
13 Amioy Kumar, MadasuHanmandlu, Harsh Sanghvi, & Gupta H
M, Decision Level Biometric Fusion Using Ant ColonyOptimization, in 17th Inter Conf on Image Process (Hong Kong)
2010.
14 Pengfei Yu, Dan Xu & Hao Zhou, Feature Level Fusion Using
Palmprint and Finger Geometry Based on Canonical Correlation
Analysis, in Inter Conf on Adv Computer Theory and Engine,
2010.
15 Yong Jian Chin, Thian Song Ong, Michael K.O. Goh & Bee Yan
Hiew, Integrating Palmprint and Fingerprint for Identity
Verification, in Third Inter Conf on Network and System Security,
2009.
16 Xiao-Yuan Jing, Yong-Fang Yao, David Zhang, Jing-Yu Yang &
Miao Li, Face and palmprint pixel level fusion and Kernel DCV-
RBF classifier for small sample biometric recognition, Pattern
Recognit,40
(2007) 3209 – 3224.17 Wang J, Li Y, Ao X, Wang C & Zhou J, Multi-modal biometric
authentication fusing iris and palmprint based on GMM, in
IEEE 15th Workshop on Statistical Signal Process, 2009,
349-352.
18 Rao K R &Yip P, Discrete Cosine Transform: Algorithms,
Advantages, Applications, ( Academic) 1990.
19 Hafed Z M & Levine M D, Face Recognition Using the Discrete
Cosine Transform, Intl J Computer Vision, 43 (2001) 167-188.
20 Kolen J F, Fool’s gold: Extracting finite state machines from
recurrent network dynamics. in Cowan, J. D., Tesauro, G., and
Alspector, J., editors, Advances in Neural Information Processing
Systems, 6 (1994) 501–508.
21 Goldberg D E, Genetic Algorithms in Search, Optimization and
Machine Learning ,(Addison-Wesley, Reading, MA)1989.22 Kala R, Vazirani H, Shukla A & Tiwari R, Fusion of Speech and
Face by Enhanced Modular Neural Network, Information
Systems, Technol Manage, Communications in Computer and
Information Sci, 54 (2010) 363-372.
23 Daugman J G, Uncertainty Relation for Resolution in Space,
Spatial-Frequency, and Orientation Optimized by Two
Dimensional Visual Cortical Filters, J Optical Soc Am., 2 (1985)
1160-1169.
24 Giot R, Hemery B& Rosenberger C, Low cost and usable
multimodal biometric system based on keystroke dynamics and
2d face recognition, in IAPR Inter Conf on Pattern
Recognit,(Istanbul, Turkey) 2010
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