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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: [email protected]

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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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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… (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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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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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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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.

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