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CHAPTER 5

PALMPRINT RECOGNITION WITH ENHANCEMENT

5.1 INTRODUCTION

This chapter discusses the application of enhancement technique in

palmprint recognition system. Section 5.2 describes image sharpening

methods. Advanced palmprint recognition system is discussed in Section 5.3.

A palmprint recognition using Symbolic Aggregate approximation (SAX)

features is discussed in Section 5.4. Palmprint recognition using multiple

palmprint features with enhanced palmprint image is given in Section 5.5.

The performance measures are given in Section 5.6. The results are discussed

in Section 5.7 .The performance of the proposed enhancement technique is

compared with the HE technique. Summary of the chapter is given in Section

5.8.

There exist three different categories for palmprint authentication

(Zhang ,2004), divided on the basis of extracted features; (i) line-based

approaches (Han et al 2003, Zhang et al 1999, Han et al 2007 ) (ii)

appearance-based approaches (Lu et al 2003) and (iii) texture-based

approaches .

Line-based methods are based on line matching, line detection,

crease detection, morphological operators etc. Morphological operators (Han

et al 2003) can be used in the feature extraction process in order to obtain the

feature vectors. Datum point invariant and the line feature matching technique

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(Zhang et al 1999) for the verification process are not appropriate for many

on-line security systems, since it is difficult to extract principal lines from a

low resolution palmprint image. The required recognition rates and

computational efficiency are not fully achieved here. Other issues in the line-

based approaches include the existence of similar line features in many

palmprints, and the thickness and width of the different lines are not taken

into account which is very critical for differentiating palmprints.

Appearance-based approaches are based on the analysis of principal

component and linear discriminate. The original training palmprints are

transformed into small groups of characteristic feature images called

eigenpalms (Lu et al 2003) by means of Karhunen–Loeve (K–L) transform.

But the authentication performance of these eigenpalms is reduced when it is

applied in real time applications.

In texture-based approach, texture features are extracted by means

of gabor filter, discrete cosine transform, ordinal filter, laws mask, discrete

fourier transform, wavelets and so on. Even though the Gabor filter (Kong et

al 2003) gives accurate time-frequency location and robustness against

varying image’s brightness and contrast, it involves high computational cost

and time delay. Difficulty in setting an appropriate filter parameter causes

identification performance to depend much on the training set used for

parameter selection. Orthogonal line ordinal feature (Sun et al 2005) is

currently the best ordinal measure for palmprint representation; still its

theoretical foundation has not been well formulated. Wavelet Energy Feature

technique’s (Wu et al 2002) ability to distinguish palms is very strong because

it can reflect the wavelet energy distribution of the principal lines, wrinkles

and ridges in different directions at varying wavelet decomposition levels.

Disadvantages of this method are its sensitiveness towards noise, the

requirement of high resolution images, implementation cost and time

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complexity. Fourier transform method (Li et al 2002) is used to ignore the

abundant textural details of a palm, and the extracted features are highly

influenced by the lighting situations. A huge storage of database to store

image templates affects the system’s simplicity.

Personal verification using palmprint and hand geometry biometric

(Kumar et al 2003) does verification by integrating hand geometry features.

Palmprint and hand geometry features are acquired using a digital camera.

These images are aligned and then used to extract palmprint and hand

geometry features. These features are then examined for their individual and

combined performances. The image acquisition setup used in this work is

inherently simple and it does not employ any special illumination, nor does it

use any pegs to cause any inconvenience to the users.

Hierarchical palmprint identification via multiple feature extraction

(You et al 2002) describes a new method to authenticate individuals based on

palmprint identifcation and verification. A texture-based dynamic selection

scheme is used to facilitate the fast search for the best matching of the sample

in the database in a hierarchical fashion. The global texture energy, which is

characterized with high convergence of inner-palm similarities and good

dispersion of inter-palm discrimination, is used to guide the dynamic selection

of a small set of similar candidates from the database at coarse level for

further processing. Multiple palmprint features are used by Nanni and Lumini

(2009) for accurate palmprint recognition.

Combining multiple classifiers by averaging or by multiplying (Tax

et al 2000) combines observations from different sources. Palmprint-based

recognition system using phase difference information (Badrinath et al 2012)

uses histogram equalization for enhancing the contrast of the palmprint

image. Most of the works did not give any attempt to incorporate the

enhancement techniques in biometric palmprint images to achieve

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performance improvement. In most of the prior systems, the enhancement

stage got little attention. The enhancement of the input image can deeply

affect the output of the system. This work incorporates curvelet and RHE

techniques for enhancement of the palmprint image. Curvelet is used for

removing noise in the palmprint image, and RHE is used for improving the

contrast of the palmprint image. Applying curvelet and RHE improves the

overall performance of the system.

5.2 IMAGE SHARPENING

Image sharpening refers to any enhancement technique that

highlights edges and fine details in an image.

5.2.1 Highpass Filter

The highpass filter allows high frequency data to pass through,

suppressing low frequency data. Highpass filtering is useful for finding edges,

for enhancing lines and for sharpening an image. Small window size will

enhance small features and details. Large window size will allow large

features to pass through, suppressing or eliminating smaller features.

In general, image filters operate by performing a mathematical

operation on each pixel using the pixels surrounding it to generate the result.

For instance, a low pass filter changes the value of each pixel in the image to

the average of it and the pixels in its neighborhood. This average replaces the

gray level of the selected pixel gray level.

The chosen window size determines how big that neighborhood is.

For instance, a 3x3 size has eight pixels all round the sample pixel in the

center of the window. The windows move through every pixel in the image,

performing the same mathematical operation using the surrounding pixels in

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its neighborhood to determine the target pixel’s new value. The mask for the

highpass filter is given in Figure 5.1

-1 -1 -1

-1 9 -1

-1 -1 -1

Figure 5.1 3x3 mask for highpass filter

This filter can be viewed as a kind of slope filter in which it

highlights pixels that have values that are different from their neighboring

pixels. The greater the difference, the higher or lower (visually brighter or

darker) the output value will be.

5.2.2 Unsharp Masking

The standard tool used for sharpening in order to make edges clear

and distinct is known as UM filter. An UM filter cannot create additional

detail, but it helps to emphasize texture and greatly enhance the appearance of

detail. It performs sharpening using a procedure that subtracts an unsharp or

smoothed version of an image from the original image. The unsharp filtering

technique is also used in other areas such as photographic and printing

industries. Unsharp masking filter produces an edge image g(x,y) from an

input image f(x,y) as given in Equation(5.1)

g(x,y) = f(x,y) – fsmooth(x,y) (5.1)

where fsmooth (x,y) is a smoothed version of f(x,y). The complete unsharp

sharpening operator for the UM filter is shown in Figure 5.2 below.

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Figure 5.2 The UM filtering operator

The output of the UM filter is given in Equation (5.2)

fsharp(x,y) = f(x,y) + k * g(x,y) (5.2)

where k is a scaling constant. Reasonable values for k vary between

0.2 and 0.7, with the larger values providing higher rate of sharpening.

5.3 ADVANCED PALMPRINT RECOGNITION SYSTEM

The system has two phases, namely enrollment phase and

identification phase. During enrollment phase, the system will store the

multiple features in the database. During the identification phase, the features

are extracted from the input image using the same algorithm and are

compared with the features in the database.

This study proposes a highly reliable method called advanced

palmprint recognition using curvelet transform and RHE to overcome the

drawbacks of the existing palmprint authentication systems and ensures high

accuracy and efficiency. The block diagram of the advanced palmprint

recognition system is given in Figure 5.3. Various steps involved in the

proposed method are palmprint acquisition, enhancement, feature extraction

and palmprint matching. The noise in the input image is removed by curvelet

transform and then equalized by RHE technique. The features are extracted

from the enhanced image. Two sets of features are extracted in this work, (i)

SAX features and (ii) multiple palmprint features.

Smooth + +f(x,y)

fsharp(x,y)

g(x,y) -

+ +

+

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Figure 5.3 Block diagram of the advanced palmprint recognition system

5.4 PALMPRINT RECOGNITION USING SAX FEATURES

The proposed palmprint recognition system using SAX features is

shown in Figure 5.4. It composes input acquisition, enhancement, feature

extraction and recognition stages. The input acquisition and enhancement

steps are discussed in chapter I, II, and III. This chapter mainly focuses on the

feature extraction and recognition steps. The block diagram of palmprint

recognition using SAX features is shown in Figure 5.4.

Accept

Reject

Palmprint image

Image enhancement

Feature extraction

Store in Database

Palmprint image

Image enhancement

Feature extraction

Palmprint matching

Decision

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Figure 5.4 Block diagram of palmprint recognition using SAX features

Here, an extension of the SAX is used which represents the 2D data

using a 2D matrix of symbols. The method uses the gray scale information of

captured images in order to reduce the complexity of execution.

5.4.1 Introduction to SAX

The symbolic demonstration of time series is called SAX. It

transforms the original time-series data into symbolic strings where Piecewise

Aggregate Approximation (PAA)-based algorithm is used that provides

simplicity and low computational complexity. Since SAX requires less

Reject

Database MINDIST calculation

Matching Result

Accept

Input image

Gray scale conversion

Curvelet transform

RHE Division of image into

blocks

Find mean for each

block

Calculate threshold to determine

breakpoints

Convert the values into

symbols

2Ddecomposition

into 1D sequence

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storage space, it is used for solving many challenges associated with the

present data mining tasks. In addition, the symbolic representation allows

researchers go further to the available wealth of data structures and string

manipulation algorithms in computer science, and also for many applications

in bioinformatics and data mining (Wang et al 2004).

SAX algorithm transforms a time-series X of length ‘n’ into the

string of arbitrary length w, where w < n, using a table that contains the

breakpoints. Each value in the table corresponds to each level in the time

series graph which is further represented by symbols in the alphabet array of

size >2 which finally forms the string of length ‘w’. Dimensionality reduction

is based on two parameters named ‘sax length’ (w) and ‘number of symbols

used in the alphabet array’. The algorithm consists of two steps. In the first

step, it transforms the original time-series into a PAA representation ( C ) and

this intermediate representation is further converted into alphabetic string ( C )

in the second step. Usage of PAA approach in the first step gives the

advantage of being simple and efficient in dimensionality reduction and

provides lower bounding property. The second step, where the actual

conversion of PAA coefficients into alphabets is also computationally

efficient, shows the contractive property of symbolic distance.

Generating SAX from PAA representation of a time series is

implemented in a way which produces symbols that correspond to the varying

magnitude of time-series data (Lin et al 2003). Figure 5.5 shows the

conversion of times series into SAX representation. Figure 5.5 (a) is the

original time series data. Figure 5.5 (b) shows the corresponding PAA

representation and Figure 5.5(c) shows the SAX representation.

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(a) (b)

(c)

Figure 5.5 Times series to SAX representation (a) Time series (b) PAA representation (c) SAX representation

5.4.2 2D SAX Representation for the Proposed System

The size of the gray scale image matrix Q, is m x n. It is then

divided into equal size blocks of size w1 x w2 and the mean value of the data

inside each block is calculated to form the dimensionality reduced

representation. The newly formed mean value matrix is named as Q of size

w1 x w2. The (ith, jth) element of Q or the mean value of each block in Q can

be calculated by using the Equation (5.3)

iwm

11iwmx

jwn

11jwny21

1

1

2

2

yx,Qw

1j)(i,Qw

(5.3)

Breakpoints have to be applied to convert Q into a symbol matrix S

or the 2D SAX representation. For this, a ‘threshold’ value is used so that

breakpoints can be calculated easily. Threshold value is calculated using the

Equation (5.4)

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SAX

minmaxTlevel

(5.4)

where ‘max’ is the maximum value in the Q matrix, ‘min’ is the minimum

value in the Q matrix, and SAXlevel is equal to number of symbols to be used.

For example, if four SAXlevel are chosen, five breakpoints have to

be calculated. The breakpoints are min, min+T, min+2T, min+3T and max.

Then, one symbol is used to represent the values between min and min+T,

and another symbol is used for the values between min+T and min+2T and so

on. In this manner, the symbol matrix S, the 2D SAX representation can be

obtained. Then, this two dimensional data is decomposed into one

dimensional (1D) sequences using a progressive scan (Chen et al 2010).

These 2D SAX representations as strings are stored in the database as

palmprint template.

5.4.3 Palmprint Matching

The MINDIST between two corresponding strings is measured and

this is adopted as the similarity measurement of the corresponding two

palmprints. Given two SAX strings 21ww1211 q,...q,qQ and

21ww1211 c,...c,cC ,

their MINDIST can be calculated using the Equation (5.5) where dist() can be

implemented using lookup table as given in Figure 5.1. If the MINDIST is

smaller, then the two palmprints are more similar.

2)1w

1i2w

1j ijc,ijqdist(2w1w

mn)C,QMINDIST( (5.5)

where m x n is the size of the image and w1 x w2 is the SAX length.

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Table 5.1 Lookup table for calculating dist() function with four SAXlevel

a b c da 0 0 0.67 1.34

b 0 0 0 0.67

c 0.67 0 0 0

d 1.34 0.67 0 0

234 subjects from IIT Database are used for recognition. Four

samples are taken for each subject. The features of the palmprint image to be

recognized are checked with the features of the palmprint images stored in the

system’s database. If a match is found, then the person is authenticated ,

otherwise the person is unauthenticated. Verification is done with HE

,curvelet ,RHE and curvelet & RHE methods . It is proceeded with two

phases in which in the first phase, 117 random palmprint images from the IIT

database that are also contained in the system’s database were tested. In the

second phase, 117 palmprint images were taken for testing from the IIT

database that is not in the system’s database.

Figure 5.6 Sample palmprints

Few samples of palmprints are shown in Figure 5. 6, in which

palmprints in the same column are from the same palm, and the palmprints in

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the first and second rows are from session one and session two respectively.

Figure 5. 7 shows the output of the palmprint enhancement and feature

extraction process where block size (w1×w2) is set as 8×8 and the SAXlevel as

4. The SAX string is used as palmprint templates for experiment-matching

process. Figure 5.8 shows the four samples of a palmprint from an individual

and their corresponding SAX representation.

(a) (b) (c) (d)ddbbdddccccbbbbbddbbcddbbcbbccccddbcddcccccbccccdcbbcccbbbcbcbcccbbbbbbbbbbbbbbccbbcccabbbbbbbbbdbccccbbbbbbbabbcbccdbccbbbaaabacbcdcbccccbaaaaacccdbcccccbbaabcbcdcbcccdcbbbbbcccdbdddccbbbbbccdddbddcbccbabbcccdcc

dcbcccabcccbccbcdbccbbaaccccccbcbccccbaacccc (e)

Figure 5.7 Palmprint enhancement and feature extraction. (a) The input image. (b) image after applying curvelet (c) RHE equalized image (d) SAX representation (e) SAX string

(a) (b) (c) (d)

(e) (f) (g) (h)Figure 5.8 (a),(b),(c) and(d) palmprints from the same palm, (e),(f),(g)

and (h) corresponding SAX representation

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5.5 PALMPRINT RECOGNITION USING MULTIPLE

PALMPRINT FEATURES

This system uses a combination of four major representations of the

palmprint. i.e Discrete Cosine Transform (DCT), Local Binary Pattern (LBP),

GB (Gabor features) and line features. The sum rule is used for fusing

multiple features of the palmprint. Decision will be taken based on the

distance between the input image features and features of the images in the

database. Here, the first stage of the system is to perform the enhancement of

the palmprint image. Curvelet, RHE and highpass filter are used for

enhancement of the palmprint image.

The second stage of the system is feature extraction. The system

extracts multiple features from palmprint image. The output of the feature

extraction process is the feature vector with variance. These feature vectors

can be used for the unique representation of a palmprint image. Figure 5.9

shows the block diagram of feature extraction process.

Figure 5.9 Block diagram of feature extraction stage

Enhanced image

Feature extraction

GB LBP DCT Line features

Store the feature vectors

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5.5.1 Extraction of Gabor Features

Gabor filter is a linear filter used for edge detection. Frequency and

orientation representations of Gabor filters are similar to those of the human

visual system, and they have been found to be particularly appropriate for

texture representation and discrimination. In the spatial domain, a 2D Gabor

filter is a Gaussian kernel function modulated by a sinusoidal plane wave. The

Gabor filters are self-similar. Figure 5.10 shows the Gabor Filters used for

feature extraction. By using Gabor filter, system extracts texture features

which can be employed for palmprint verification.

Palmprint image is filtered with a bank of 36 Gabor filters to

generate 36 filtered images. Here, the orientation is varied from 0 to 2 with

10 degree increments. Kong et al (2003) described that filters with frequency

0.0916 and standard deviation 5.6179 achieved high recognition rate. Hence,

the frequency is chosen as 0.0916 and sigma is chosen as 5.6179. Each of the

filtered images are overlaid one over the other so that line features in all

directions get enhanced. The components of palmprint crease and lines in

different directions are captured by each of these filters. The extracted

prominent texture features can be used for unique representation of a

palmprint image. Figure 5.11 shows the extracted Gabor feature by

superimposing all the Gabor-filtered images.

Figure 5.10 Gabor filters

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Figure 5.11 Gabor features

Figure 5.12 Gabor feature vector

Gabor feature vector template is created using the technique of

dimensionality reduction. The dimension of the image gets reduced in such a

way that nine pixels (one pixel surrounded by eight pixels) get reduced to a

single pixel and its value will be the average of the surrounding pixel. This is

the simple technique which uses the mean of the block for dimensionality

reduction. The technique of dimensionality reduction helps us to rectify the

rotational invariance of the input palmprint images and reduces the

complexity. Thus the system can accurately identify the input palmprint

image. Gabor feature vector after dimension reduction is given Figure 5.12.

5.5.2 Extraction of DCT Features

A DCT expresses a sequence of finitely many data points in terms

of a sum of cosine functions oscillating at different frequencies. DCT is a

separable linear transformation. The two-dimensional DCT is equivalent to a

one-dimensional DCT in single dimension followed by a one-dimensional

DCT in the other dimension. The salient information of the DCT exists in the

coefficients with low frequencies. Because of this property of the DCT, most

161

DCT components are typically very small in magnitude. Discarding the small

coefficients from the DCT representation, only a small error is resulted.

Figure 5.13 (a) is a sample image and the enhanced image is given in Figure

5.13(b). Figure 5.13 (c) shows the DCT features.

DCT coefficients with higher variance are retained, and remaining

small coefficients which introduce a small error in the reconstructed image

from the DCT representations are discarded. For dimensionality reduction, the

first N co-efficients are retained.

(a) (b) (c)

Figure 5.13 (a) Original image (b) Enhanced image (c) DCT features

5.5.3 Extraction of LBP Features

LBP is a simple yet very efficient texture operator which labels the

pixels of an image by thresholding the neighborhood of each pixel and

considers the result as a binary number. Due to its discriminative power and

computational simplicity, LBP texture operator has become a popular

approach in various applications. The most important property of the LBP

operator in real-world applications is its robustness to monotonic gray-scale

changes. Another important property is its computational simplicity, which

makes it possible to analyze images in challenging real-time settings. Figure

5.14 shows LBP feature and its histogram.

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(a) (b)

Figure 5.14(a) LBP features (b) LBP histogram

5.5.4 Extraction of Lines and Ridges

The lines and ridges are unique for each palmprint image. The

palmprint image will contain mainly three principal lines, namely heart line,

head line and life line. In addition to that it has wrinkles and ridges. The

principal lines will be more prominent in palmprint images. The enhanced

image is sharpened to highlight the line features. Palmprint image is

sharpened using unsharp masking. By creating the binary image from the

sharpened image, the lines and ridges are extracted. Figure 5.15 shows the

extracted line features. Dimensionality reduction is done as in Section 5.5.1

Figure 5.15 Line features

5.5.5 Creating Template from Feature Vectors

The proposed system extracts four types of feature vectors. They

are Gabor feature vector, DCT feature vector, LBP feature vector and line

feature vector. The system stores these four templates in the database. The

sum rule is applied on the feature vectors before the distance calculation takes

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place. The feature vector of the input palmprint image is verified against

feature vector of the palmprint image in the database.

If nxxx ,.....,X 21 is one feature vector and nyyy ,.....,Y 21 is

another feature vector, then fusion of these vectors Z using sum rule is

represented as in Equation (5.6)

n1 yxyxyx n221 ,.....,Z (5.6)

5.5.6 Palmprint Matching

This system uses Euclidian distance for distance vector calculation

for palmprint matching. The distance calculation is one of the important

things in authentication. Distance between two palmprint images shows the

similarity of those two palmprint images. The palmprint images from the

same person with negligible transposition will be of minimal distance mean,

while the distance between two palmprint images from two different people

will be high.

Euclidean distance is used for distance calculation. Euclidean

distance between vector X and Y is calculated as in Equation (5.7)

n

1i

2ii y)Y,Xd x (5.7)

Experimental results show that the extracted features from the

enhanced image is more accurate than that of the direct input image. Here, the

proposed system shows the effect of the enhancement stage to obtain more

recognition rate.

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5.6 PERFORMANCE MEASURES

The performance of a palmprint authentication system can be

measured by three metrics, namely FAR, FRR and TSR.

False Acceptance (FA) is the number of times the system accepts

an unauthorized user (imposter) and FAR is the ratio of number of false

acceptances to the total number of imposter accesses. FAR is measured as in

Equation (5.8)

%100accessesimposter ofnumber Total

sacceptancefalseofNumber FAR (5.8)

False Rejection (FR) is the number of times the system rejects an

authorized user (genuine) and FRR is the ratio of number of false rejections to

the total number of genuine accesses. FRR is measured using Equation (5.9)

%100accessesgenuineofnumber Total

ctionsfalse rejeNumber of FRR (5.9)

TSR (Connie et al 2005) represents the recognition rate of the

system and it is measured using Equation (5.10)

%100accessesofnumber Total

FRFA1TSR (5.10)

5.7 RESULTS AND DISCUSSION

5.7.1 Performance of Palmprint Recognition Using SAX Features

The comparison of FAR of the RHE & curvelet method with other

methods is given in Figure 5.16. RHE& curvelet method has the lowest FAR.

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8.547

6.8376

3.4188

1.7094

0123456789

HE RHE Curvelet RHE andCurvelet

Figure 5.16 Comparison of FAR of RHE & curvelet method with HE, RHE and curvelet

The comparison of FRR of the RHE & curvelet method with other

methods is given in Figure 5.17. RHE& curvelet method has the least FRR.

The comparison of TSR of the RHE & curvelet method with other methods is

given in Figure 5.18. RHE& curvelet method has the highest TSR. Here, the

proposed technique provides TSR of 98.29%.

10.2564

7.6923

5.9829

1.7094

0

2

4

6

8

10

12

HE RHE Curvelet RHE andCurvelet

Figure 5.17 Comparison of FRR of RHE & curvelet method with HE,

RHE and curvelet

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90.5982

92.735

95.2991

98.2905

86

88

90

92

94

96

98

100

HE RHE Curvelet RHE andCurvelet

Figure 5.18 Comparison of TSR of RHE & curvelet method with HE, RHE and curvelet

The optimum result is obtained when both curvelet and RHE are

used. The FAR, FRR and TSR values are measured as 1.7094%, 1.7094%,

98.2905%. Michael et al (2008) used contrast adjustment and smoothing as

enhancement technique in contactless palmprint and recognition system. They

obtained a recognition rate of 98.10 % without image enhancement and

recognition rate of 98.68% with image enhancement. An improvement of 0.58

% is obtained using enhancement. The proposed system provides an

improvement of 8% than the HE method using RHE and curvelet.

5.7.2 Performance of Palmprint Recognition Using Multiple

Palmprint Features

In this work, the performance of the system is analyzed by using

the FAR, FRR and TSR . For the best performance of the system, the metric

FAR and FRR should be minimum and TSR should be maximum.

The experimental results show that the performance of the system

increased, while the system incorporates enhancement before feature

extraction. This system improves the performance of the system by

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enhancement and the effective utilization of multiple features palmprint

image.

7.6923

5.1282

2.5641

0.8547

0123456789

HE RHE Curvelet RHE andCurvelet

Figure 5.19 Comparison of FAR of RHE & curvelet method with HE,

RHE and curvelet with multiple palmprint features

The comparison of FAR of the RHE & curvelet method with other

methods is given in Figure 5.19. RHE& curvelet method has the lowest FAR.

FAR value is 0.8547. The comparison of FRR of the RHE & curvelet method

with other methods is given in Figure 5.20. RHE& curvelet method has the

least FRR. The FRR value is 0.8547.

9.4017

6.837

5.1282

0.8547

0123456789

10

HE RHE Curvelet RHE andCurvelet

Figure 5.20 Comparison of FRR of RHE & curvelet method with HE, RHE and curvelet with multiple palmprint features

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91.4529

94.017

96.1538

99.1452

86

88

90

92

94

96

98

100

HE RHE Curvelet RHE andCurvelet

Figure 5.21 Comparison of TSR of RHE & curvelet method with HE,

RHE and curvelet with multiple palmprint features

The comparison of TSR of the RHE & curvelet method with other

methods is given in Figure 5.21. RHE& curvelet method has the highest TSR.

The TSR of the proposed method is 99.1452. The optimum result is obtained

when both curvelet and RHE are used.

Michael et al (2010) used homomorphic filtering technique as

enhancement technique in touchless palmprint and knuckle print recognition

system. Here, without image enhancement 96.52 % of Genuine Acceptance

Rate (GAR) and with image enhancement 98.02% of GAR is obtained. An

improvement of 1.5 % is obtained using enhancement. The proposed system

provides an improvement of 8% than the HE method using RHE & curvelet.

5.8 SUMMARY

To improve the accuracy of palmprint authentication system,

curvelet and RHE are applied prior to the 2D SAX conversion. As these

procedures remove the noise and enhance the contrast, the proposed system

ensures high performance with competitive accuracy. Since it is a texture-

based approach, the computational complexity of the feature extraction

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process is much lower and thus can be efficiently implemented for even slow

mobile embedded platforms. Also, the proposed approach does not rely on

any parameter training process. A palmprint authentication system using

multiple palmprint features with enhancement is proposed in this work. It uses

four different features from the palmprint image, namely GB, DCT, LBP and

line feature. RHE and curvelet are used for enhancement. The experimental

results demonstrated that the RHE & curvelet method achieves optimum

performance comparison with the other ones.