IJSRD - International Journal for Scientific Research & Development| Vol. 6, Issue 09, 2018 | ISSN (online): 2321-0613

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Integrated Biometrics (Face, Fingerprint, Iris, Palmprint & Ear) using

PCA

Khushboo1 Sat Pal2 1,2Baba Mastnath University, India

Abstract— Integrated Biometrics is a rising domain in

biometric technology where more than one biometric trait is

combined to improve the performance. Integrated biometrics

has much higher efficiency than unimodal biometrics.

Integrating the biometric system results in enlarge the user

database but increase the level of security. In this paper, a

study of integrated biometrics has been done with five

biometrics considered face, iris, ear, palm and finger

respectively. In biometric system, Problems arise when

integrated performing recognition in a high-dimensional

space. Significant improvements can be achieved by mapping

the data into a lower dimensionality space. This can be done

by PCA approach. PCA method reduces memory requirement

of the system and computation time. This paper presents a

method for face, iris, fingerprint, palm print and ear

recognition based on Principal Component Analysis (PCA). I

have applied the same PCA algorithm for all these biometric

traits.

Key words: PCA, Applications, Face Recognition, Iris

Recognition, Fingerprint Recognition, Palm Print

Recognition, Ear Recognition

I. INTRODUCTION

Biometrics refers to the use of physiological or biological

characteristics to measure the identity of an individual. These

features are unique to each individual and remain mostly

stable during a person’s lifetime.

Fig. 1:

These features make biometrics a promising secure

solution to the society. The access to the secured area can be

made by the use of ID numbers or password. But such

information can easily be accessed by intruders and they can

breach the doors of security. The problem arises in case of

monetary transactions and highly restricted to information

zone. Thus to overcome the above mentioned issue biometric

traits are used. The biometrics possesses unique, time

invariant, consistent and not able to be forgotten or lost

features. These features made biometrics an important

component in security-related applications such as: logical

and physical access control, airport/sea security, border

control, forensic investigation, IT security, identity fraud

protection, and terrorist prevention or detection, etc.[3].

Biometric systems work by first capturing a sample

of the feature, such as recording a digital sound signal for

voice recognition, or taking a digital color image for face

recognition or taking the print for the palmprint recognition.

The sample is then transformed into a biometric template

using some sort of mathematical function. The biometric

template will provide a normalized, efficient and highly

discriminating representation of the feature, which can then

be objectively compared with other templates in order to

determine identity.

Most biometric systems allow two modes of

operation [5]. First one is an enrolment mode for adding

templates to a database, and second one is a verification/

identification mode, where a template is created for an

individual and then a match is searched for in the database of

pre-enrolled templates [40].

Enrollment Process

Fig. 2: Enrollment Process

Fig. 3: Verification Process

The new template is matched against the stored

template and a measure M is calculated. The Decision module

accepts or rejects the claimed user using the measure M [40].

Fig. 4: Identification Process

The new template is matched against all the

templates in the database producing a score S for each

template. Depending on the score the Decision module

decides who the user is [40].

The various biometrics traits available are face,

fingerprint, iris, palm print, hand geometry and ear, thumb.

The reliability of several biometrics traits is measured with

the help of experimental results. In this study, Among the

available biometric traits some of the traits have been

considered namely face, iris, ear, palm and finger

Integrated Biometrics (Face, Fingerprint, Iris, Palmprint & Ear) using PCA

(IJSRD/Vol. 6/Issue 09/2018/165)

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respectively. Research groups around the world are

developing algorithms and systems based on face, iris,

fingerprint, palm print and ear. But I have used here PCA

algorithm for all these biometric traits.

PCA was invented in 1901 by Karl Pearson.

Principal component analysis (PCA) is a classic technique

used for compressing higher dimensional data sets to lower

dimensional ones for data analysis, visualization, feature

extraction, or data compression. The Principal Component

Analysis (PCA) is one of the most successful techniques that

have been used in image recognition and compression. The

purpose of PCA is to reduce the large dimensionality of the

data space to the smaller intrinsic dimensionality of feature

space which is needed to describe the data economically. The

PCA is a technique that allows, from extracting the

eigenvectors and eigenvalues of the covariance matrix of

images, to create a space of reduced images, which contains

the “main components” of the images for subsequent

recognition. Although PCA is widespread adopted in the

recognition of face images, we apply here this technique also

as a part of the face, iris, fingerprint, palmprint and ear

recognition [5].

It is a way of identifying patterns in data, and

expressing the data in such a way as to highlight their

similarities and differences. Since patterns in data can be hard

to find in data of high dimension, where the luxury of

graphical representation is not available, PCA is a powerful

tool for analyzing data. PCA is applied on Eigen face

approach to reduce the dimensionality of a large data set. The

advantage of PCA is that once we have found these patterns

in the data, and we compress the data, by reducing the number

of dimensions, without much loss of information. This

technique used in image compression. When the original data

is reproduced, the images have lost some of the information.

This compression technique is said to be lossy because the

decompressed image is not exactly the same as the original,

generally worse.

PCA is the concept of Eigenvalues and Eigenfaces.

PCA is a method of transforming a number of correlated

variables into a smaller number of uncorrelated variables. .

PCA can be applied to the task of face recognition by

converting the pixels of an image into a number of eigenface

feature vectors, which can then be compared to measure the

similarity of two face images. PCA involves the calculation

of the eigenvalue decomposition of a data covariance matrix

or singular value decomposition of a data matrix, usually after

mean centering the data for each attribute [2]. After feature

extraction, the image is represented as a feature vector. We

used Euclidean Distance similarity measures to measure the

similarity of test and training images feature.

II. EARLIER WORK

Three algorithms - Artificial Neural Network, Eigenfaces and

Active Appearance Model based methods for face

recognition. Eigenfaces for face representation was used first

used by Sirovich and Kirvy which was later developed

by Turk and Pentland for face recognition. Different

techniques have been developed using neural networks. The

implementation by Lawrence, Giles, Tsoi and Back showed

good results. Taylor and Cootes used Active Appearance

Model to design a system for face identification. Some

comparison has been done between eigenfaces vs.

fisherfaces, eigenfaces vs. feature based techniques and

likewise. [34] Have proposed approach to replace the Gabor

features by a graph matching strategy and HOGs (Histograms

of Oriented Gradients). [35] Have proposed the FERET

Evaluation Methodology for Face Recognition Algorithm.

[36] Have proposed Robust Facial Feature Tracking

methodology for tracking the facial features of human face.

In iris recognition , many researchers gave their

contributions, [18] proposed an improved AD Adaboost

algorithm for eye detection in an image, [19] presented a

matching strategy based on possibilistic fuzzy clustering

algorithm for iris recognition ,[20] introduced the zero

crossing detection method for iris recognition, [21] proposed

the iris recognition system based on chaos encryption , [22]

proposed an iris recognition system using phase based image

matching and presented an algorithm of wavelet transform for

iris recognition[4]. Gabor filters and discrete wavelet

transform zero-crossing representation

In fingerprint recognition, [18] proposed multilayer

perceptrons and use the elements of the orientation image as

input features. [19] Proposed a neural network as decision

stage and the fingerprint features were extracted using the

discrete wavelet transform. [13] Proposed a method using the

2D hidden Markov model. [10] The orientation image is

partitioned into regions by minimizing a cost function that

takes into account the variance of the element orientations

within each region. An inexact graph matching technique is

then used to compare therelational graphs with class-

prototype graphs.

In palmprint recognition, [23] have used Principal

Component Analysis (PCA), Fisher Discriminant Analysis

(FDA) and Independent Component Analysis (ICA) for the

feature extraction from the raw images. [24] Used the

approach of compact extraction of principle lines from the

palmprint images by using filtering operations consecutively.

Here, the image is first smoothed and then worked upon. The

palmprint images are passed through several filters.[26] have

used the two dimensional Gabor for the development of a

high performance palmprint identification. [27] Proposed an

approach which makes use of the multispectral analysis of the

hybrid features to improve the performance of the palm print

recognition system. [29] Have proposed an online palm print

identification system. This system was developed to make

authentication possible in the real time also. [30] have

proposed a novel preprocessing technique for DCT domain

palmprint recognition in which the task of feature extraction

is carried out in local zones using 2 dimensional Discrete

Cosine Transform (2D-DCT). [65] Proposed a dynamic

selection scheme by introducing global texture feature

measurement and the detection of local interesting points

In ear recognition [31] have propose force field

transformation for the force field feature extraction, [32] have

proposed local surface patch comparisons using range data ,

Voronoi diagram matching, neural networks, and genetic

algorithms. [33] Have proposed geometric feature extraction

and 2D, 3D data, ICP. [37] Detected image regions with a

large local curvature with a technique called step edge

magnitude. [38] Use weak classiers based on Haar-wavelets

in connection with AdaBoost for ear localization. [39]

Integrated Biometrics (Face, Fingerprint, Iris, Palmprint & Ear) using PCA

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developed an ear detection method which fuses range images

and corresponding 2D color images .

Here is the table shows the different algorithms used

by researchers in different biometric traits.

Algorithms used Face Finger

print Iris

Palm

print Ear

Principal

Component

Analysis (PCA)

[48] [52] [10] [16] [33]

Independent

Component

Analysis

[48] [52] [10] [16] [77]

Discrete Cosine

Transform(DCT) [49] [78] [79] [30] [119]

Linear

Discriminant

Analysis(LDA)

[50] [52] [41] [81] [82]

Locality

Preserving

Projections(LPP)

[50] ------- ------

-

------

-

------

-

Discrete wavelet

transform(DWT) [87] [83] [88] [89] [90]

Neural networks [50] [110] [111] [112] [75]

Wavelet-Curvelet

Technique [58] [93] [94] [95] [91]

2D wavelet

transform [49] [97] [98] [99] [100]

Neural networks

with Gabor filters [50] ------ ------ ------ ------

Novel Thinning

Algorithm

------

- [55] ------ [113] ------

MERIT ------

- [56] ------ ------ ------

Image

Segmentation [118] [60] [117] [115] [116]

Cluster Algorithm [121] [61] [122] [123] [120]

Weighted Linear

Embedding

(WLE)

------ [52] ------ [124] ------

local discriminant

embedding

analysis (LDE)

------ [124] ------ [124] ------

LGIC technique ------ [52] ------ [52] ------

Gabor filter [104] [86] [103] [124] [101]

Edge detection [125] [52] [127] [126] [76]

Gabor Wavelet

Transforms [96] [105] [42] [68] [106]

Log gabor

wavelets [107] [59] [10] [108] [109]

Circular

Symmetric Filters ------ ------ [43] ------ ------

Haar Wavelet

transform [49] [128] [46] [129] [38]

Self-organizing

map neural

network

[130] [132] [47] [131] [133]

M-ICA [136] [137] [44] ----- -----

Phase Based

Image Matching [134] [85] [22] [80]

Indexing

technique ----- [139] [138] [63] -----

Competitive

Coding Scheme ----- ----- ----- [66] -----

Minutiae

extraction [140] [141] ----- [69] -----

Latent Semantic

Analysis [150] ----- ----- ----- -----

ICP [142] ----- ----- ----- [71]

Geometric

approach [143] [144] ----- ----- [40]

force field

transformations ----- ----- ----- ----- [73]

novel graph

matching based

algorithm

----- ----- ----- ----- [74]

Elastic bunch

graph matching [148] ----- [149] ----- -----

Cumulative Sums

based Approach ----- ----- [79] ----- -----

Kernel PCA [48] ----- [146] [92] [145]

Image Processing [147] ----- ----- [114] -----

CVQ technique [135] ----- ----- ----- -----

Table 1: Shows the Different Algorithms used in Different

Biometric Traits

III. APPLICATIONS

A. Applications of Biometrics

Applications of biometric system are : Security (access

control to buildings, airports/seaports, ATM machines and

border checkpoints ,computer/ network security , email

authentication on multimedia workstations),Surveillance (a

large number of CCTVs can be monitored to look for known

criminals, drug offenders, etc. ),General identity verification

(electoral registration, banking, electronic commerce,

identifying newborns, national IDs, passports, drivers’

licenses, employee IDs), Criminal justice systems (mug-

shot/booking systems, post-event analysis, forensics),Image

database investigation ,Smart Card applications , Multi-

media environments with adaptive human computer

interfaces , Video indexing ,Witness face reconstruction [25].

B. Applications of PCA

PCA is a useful statistical technique that has found

application in fields such as face recognition and image

compression. The jobs which PCA can do are prediction,

redundancy removal, feature extraction, visualization and

data compression, etc. PCA is a common technique for

finding patterns in data of high dimension. It covers standard

deviation, covariance, eigen vectors and eigen values. These

are mathematical concepts that will be used in PCA. Now a

day, PCA is using for most of the biometric traits like face,

hand, fingerprint, iris, ear, palmprint, thumb etc.

IV. HOW PCA WORKS

Suppose Γ is an N2x1 vector, corresponding to an NxN face

image I [1].

1) Step 1: obtain face images I1, I2, ..., IM (training faces)

2) Step 2: represent every image Ii as a vector Гi

3) Step 3: compute the average face vector Ψ:

Integrated Biometrics (Face, Fingerprint, Iris, Palmprint & Ear) using PCA

(IJSRD/Vol. 6/Issue 09/2018/165)

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4) Step 4: subtract the mean face:

i i

5) Step 5: compute the covariance matrix C:

Where A [1 2 . . . M] (N²xM matrix)

6) Step 6: compute the eigenvectors ui of AAT

(The matrix AAT is very large)

Step 6.1: consider the matrix ATA (MxM matrix)

Step 6.2: compute the eigenvectors vi of ATA

Thus, AATand ATA have the same eigenvalues and their

eigenvectors are related as follows: ui = Avi .

Step 6.3: compute the M best eigenvectors of AAT : ui =

Avi

7) Step 7: keep only K eigenvectors (corresponding to the

K largest eigenvalues)

Each face (minus the mean) Φi in the training set can be

represented as a linear combination of the best K

eigenvectors:

Each normalized training face Φi is represented in this basis

by a vector:

Image recognition using Euclidean distance in PCA:

follow these steps:

1) Step 1: normalize :

2) Step 2: project on the eigenspace

3) Step 3: represent as:

4) Step 4: find 𝑒𝑟 𝑚𝑖𝑛𝑙 ||𝛺𝑙 ||

5) Step 5: if er < Tr, then is recognized as face l from the

training set.

The distance er is called distance within the face space. We

use the common Euclidean distance to compute er.

A. Flowchart of PCA

Fig. 5: Flowchart

Here is the flowchart to present how the MATLAB software

program works for all Biometrics:

B. Flowchart of Proposed System

Fig. 6: Flowchart of Proposed System

V. DESCRIPTION OF BIOMETRICS USING PCA

A. Face Recognition using PCA

The task of recognition of human faces is quite complex. The

human face is full of information but working with all the

Integrated Biometrics (Face, Fingerprint, Iris, Palmprint & Ear) using PCA

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information is time consuming and less efficient. It is better

to get unique and important information and discards other

useless information in order to make system efficient. Face

recognition systems can be widely used in areas where more

security is needed. M.A. Turk and Alex P. Pentland

developed a near real time Eigen faces system for face

recognition using Euclidean distance.

The eigenfaces approach is now largely superceded

in the face recognition literature. A set of eigenfaces can be

generated by performing a mathematical process

called principal component analysis (PCA) on a large set of

images depicting different human faces. eigenfaces can be

considered a set of "standardized face ingredients", derived

from statistical analysis of many pictures of faces. Any

human face can be considered to be a combination of these

standards faces [6].

There are two phases in the identification phase:

training and testing. In the training phase the eigenvalues and

eigenvectors of the training set are extracted and the

eigenvectors are chosen based on the top eigenvalues.

Training set is a set of clean images without any duplicates.

In the testing phase the algorithm is provided a set of known

faces and a set of unknown faces as the probe set. The

algorithm matches each probe to its possibly identity in the

gallery [14]

I have used Frontal face dataset which is Collected

by Markus Weber at California Institute of Technology, 450

face images with resolution 896 x 592 pixels in the Jpeg

format, Belongings to 27 or so unique people under with

different lighting/ expressions/ backgrounds. In our

experimental result, we have used 5 individuals with different

5 face images. So, we have 25 training face images and 10

test images (5 individuals with different 2 face images). By

using PCA, we reduced the dimensions from (896 x 592) to

(56 x 56). First largest 21 eigen values are selected for 21

eigen faces instead of 25 eigenface. The Euclidean distance

is used to find out the distance between the test image and

training images. The training image which has the minimum

distance with the test image is supposed to be an individual

with perfect match. The proposed matlab program works in

jpeg format images.

I have applied the MATLAB program on 10 test

images, and all are recognized with exact individual. One

result with proper match is shown below:

Fig. 7:

The left image is the test image and it is recognized

by the right side image from the training images.

Table showing the distance between test image and

all training images. The test image has the minimum distance

with image 2 by minimum distance 1.619653077917682

Image

s

Minimum

Distance

Image

s

Minimum

Distance

1 3.6631152227755

05 14

3.6459816846536

34

2 1.6196530779176

82 15

7.5263106674452

26

3 3.1061940715067

31 16

6.2199686211636

78

4 4.2750330886209

18 17

3.5396720554887

23

5 3.4398250859590

34 18

3.8162938957098

51

6 2.5940748800938

42 19

3.9704657945910

42

7 2.2647439367216

11 20

4.3542686719853

32

8 4.4588529935605

16 21

3.1569922546851

93

9 4.9149855473645

22 22

5.4177434028974

36

10 3.1982077633311

67 23

4.2460535400070

97

11 5.2421629199057

21 24

5.3197437439429

76

12 3.4329490096996

86 25

3.7289095208330

27

13 3.7766899246178

94

Table 2: Distance between Test Image and Training Images

The graph shows the distance found between the test

images and training images. The distance is found by using

the Euclidean distance method.

Fig. 8: shows the graph plot distance found with test image

verses training images

B. Fingerprint Recognition using PCA

Fingerprints are currently emerging as the widespread

biomedical identification method in both forensic and civilian

applications. Fingerprint recognition is one of the most

reliable and valid personal recognition method which has

been in use of long time. Fingerprint probably is the most

widely used as a personal identification tool. It has been used

for many centuries, primarily because of its individuality and

0 5 10 15 20 25 300

1

2

3

4

5

6

7

8

images

min

imum

dis

tanc

e m

atch

ed

matches 2.000000, score 1.619653

Integrated Biometrics (Face, Fingerprint, Iris, Palmprint & Ear) using PCA

(IJSRD/Vol. 6/Issue 09/2018/165)

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uniqueness and reliability. Of all the Biometrics, fingerprint

identification is one of the most famous and publicized

biometrics and has been successfully used in security

applications. Nowadays, the automatic fingerprint

identification algorithms are very expansive on

computations. Due to the large size of present day fingerprint

databases, it is necessary to use methods to reduce the number

of one-to one fingerprint comparisons when performing the

fingerprint query execution. A fingerprint is believed to be

unique to each person. Fingerprints of even identical twins

are different [6].

The fingerprint image is made up of pattern of ridges

and valleys; they are the replica of the human fingertips. The

fingerprint image represents a system of oriented texture and

has very rich structural information within the image. This

flow-like pattern forms an orientation field extracted from the

style of valleys and ridges called minutia. Now days, Many

algorithms are used for feature extraction and pattern

matching for fingerprint recognition. But we have used PCA

for fingerprint recognition because of its simplicity and less

computational work and require less space in the system

because of its low dimensionality approach [7].

I have used fingerprint database. All images are in

PNG format. There are 16 people with different 8 fingerprint

images with different lighting/ expressions/ backgrounds and

resolution is (248 x 338) pixels. In the experimental result, I

have used 6 individuals with different 5 fingerprint images.

So, I have 30 training fingerprint images and 12 test images (

6 individuals with different 2 fingerprint images). By using

PCA, I reduced the dimensions from (248 x 338) to (100 x

100). First largest 24 eigen values are selected. The PNG

format of images has changed to jpeg. The proposed matlab

program works in jpeg format images. The Euclidean

distance is used to find out the distance between the test

image and training images. The training image which has the

minimum distance with the test image is supposed to be an

individual with perfect match. If I are decreasing the

dimensions and increasing the eigenvalues from 24 to 29 then

the distance also decreases.

Applied the MATLAB program on 12 test images,

and all are recognized with exact fingerprint match. One

result with proper match is shown below

Fig. 9:

The left image is the test image and it is recognized

by the right side image from the training images.

Table showing the distance between test image and

all training images. The test image has the minimum distance

with image 2 by minimum distance 2.889597219692923.

Imag

es

Minimum

Distance

Imag

es

Minimum

Distance

1 3.7344410007568

68 16

3.5031131625998

504

2 2.8895972196929

23 17

3.5947058692960

74

3 3.5139404691032

436 18

3.6730570935262

04

4 3.2168469448116

754 19

3.1267284655385

676

5 3.9914547291484

817 20

3.5490203998781

964

6 3.7082450605064

285 21

3.5557397183066

333

7 3.4197704051786

713 22

3.5508943764682

72

8 5.2262852140173

81 23

4.0814273476168

8

9 3.4797088181246

41 24

3.7254612036702

826

10 4.9884213907681

15 25

4.0502310729967

79

11 5.2931814160943

675 26

4.3637751110559

595

12 4.3699001142691

200 27

3.7332782686689

94

13 4.4212664157955

23 28

3.9223158270055

05

14 4.3156793504587

965 29

3.7942697772504

568

15 3.7695229587313

32 30

3.4325023035618

925

Table 3: Distance between Test Image and Training Images

The graph shows the distance found between the test image

and training images. The distance is found by using the

Euclidean distance method.

Fig. 10: shows the Graph Plot Distance found with Test

Image verses training images.

C. Iris recognition using PCA

Iris pattern recognition is one of the most reliable, secure and

promising approaches for individual authentication. Iris

recognition is considered as a most reliable and accurate

method because of its high recognition rate and uniqueness.

Among all biometrics, iris recognition is the most consistent

Integrated Biometrics (Face, Fingerprint, Iris, Palmprint & Ear) using PCA

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one. The iris is a thin circular diaphragm, which lies between

the cornea and the lens of the human eye. The pattern of the

human iris differs from person to person; there are not even

two irises alike, not even for genetically identical twins. The

iris is considered one of the most stable biometric, as it is

believed to not change The iris is considered one of the most

stable biometric, as it is believed to not change [8].

Various algorithms have been applied for feature

extraction and pattern matching processes .These methods

use local and global features of the iris. A great deal of

advancement in iris recognition has been made through these

efforts. Here, I have used PCA for feature extraction. In PCA,

The aim of feature extraction is to find a transformation from

an n-dimensional observation space to a smaller m

dimensional feature space [9]. Main reason for performing

feature extraction is to reduce the computational complexity

for iris recognition. Most existing iris recognition methods

are based on the local properties such as phase, shape, and so

on. However, iris image recognition based on local properties

is difficult to implement. Principal component analysis can

produce spatially global features [10]. The original data are

thus projected onto a much smaller space, resulting in data

reduction. Using PCA the feature vector is generated. The

matching of test iris and data base iris is performed using

PCA.

I have used MMU iris database images with

resolution 320 x 240 pixels in the bitmap format, Belongings

to 46 unique people with different 5 left and 5 right iris

images .In the experimental result, I have used 3 individuals

with different 3 left iris images and 3 right iris images . so, I

have 18 training iris images and 12 test images ( 3 individuals

with different 2 left iris images and 2 right iris images). By

using PCA , I reduced the dimensions from (320 x 240) to (56

x 56). First largest 15 eigenvalues are selected. The bitmap

format of images has changed to jpeg. The proposed matlab

program works in jpeg format images. The Euclidean

distance is used to find out the distance between the test

image and training images. The training image which has the

minimum distance with the test image is supposed to be

perfect iris match.

I have applied the MATLAB program on 12 test

images, and all are recognized with exact iris match. One

result with proper match is shown below.

Fig. 11:

The left image is the test image and it is recognized

by the right side image from the training images.

Table showing the distance between test image and

all training images. The test image has the minimum distance

with image 6 by minimum distance 0.5912620231613743.

Imag

es

Minimum

Distance

Imag

es

Minimum

Distance

1 1.4207929915162

376 10

1.7666360015230

704

2 2.1598878288685

32 11

1.9892906756400

18

3 1.4592054612753

664 12

2.2982665543640

395

4 2.4132833806738

314 13

3.1598112106653

033

5 1.6212619444271

357 14

3.4114385260840

48

6 0.5912620231613

743 15

3.4939594550225

124

7 2.8226185759142

02 16

2.8259585113782

997

8 1.9817126868927

14 17

2.7088968349929

3

9 2.3262217457647

383 18

2.3963525707589

04

Table 4: Distance between test image and training images.

The graph shows the distance found between the test

images and training images. The distance is found by using

the Euclidean distance method.

Fig. 12: shows the graph plot distance found with test image

verses training images

D. Palmprint recognition using PCA

Palmprint is one of the relatively new physiological

biometrics because of its stable and unique characteristics.

The rich texture information of palmprint offers one of the

powerful means in personal recognition. Palmprint

recognizes a person based on the principal lines, wrinkles and

ridges on the surface of the palm. These line structures are

stable and remain unchanged throughout the life of an

individual. More importantly, no two palmprints from

different individuals are the same, and people feel easy to

have their palmprint images taken for testing. Therefore

palmprint recognition offers promising future for medium-

security access control system [16]. Palmprint recognition is

a very interesting research area.

In Palmprint recognition, it is sometimes difficult to

extract the line structures that can discriminate every

individual well. Besides, creases and ridges of the palm are

always crossing and overlapping each other which

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complicates the feature extraction task [16]. An important

issue in palmprint recognition is to extract palmprint features.

Among the works that appear in the literature are eigenpalm

, Gabor filters , Fourier Transform , and wavelets . I have used

“eigenpalm” (PCA) in the study. Eigenpalm are just like the

eigenface. It works with the same manner as for face

recognition system. The features are extracted with PCA and

then matching process is done [62].

I have used hand database with different 300 hand

images with resolution 230 x 260 in the tif format .In the

experimental result, I have used 5 individuals with different

4 palmprint images. So, I have 20 training images and 10 test

images (5 individuals with different 2 palmprint images). By

using PCA, I reduced the dimensions from (230 x 260) to

(100 x 100). First largest 12 eigen values are selected for 12

eigenpalms instead of 20 eigenpalms. The tif format of

images has changed to jpeg. The proposed matlab program

works in jpeg format images The Euclidean distance is used

to find out the distance between the test image and training

images. The training image which has the minimum distance

with the test image is supposed to be perfect match.

I have applied the MATLAB program on 10 test

images, and all are recognized with exact palmprint match.

One result with proper match is shown below:

Fig. 13:

The left image is the test image and it is recognized

by the right side image from the training images.

Table showing the distance between test image and

all training images. The test image has the minimum distance

with image 14 by minimum distance 1.0786326202370273.

Imag

e Minimum Distance

Imag

e Minimum Distance

1 6.80975829423400

2 11

4.94269604254555

35

2 6.44992519346678

9 12 5.87120416077242

3 6.73476982962015

75 13

5.72429021168663

2

4 6.73480009255091

7 14

1.07863262023702

73

5 6.72736976435878

4 15

1.74444191962936

41

6 4.67673702502589

5 16

3.70912160631344

67

7 4.30099505853018

2 17

1.91043167035315

99

8 5.41776481775816

9 18

5.19886924455541

5

9 5.11118192213683 19 5.39035507687603

75

10 3.56827986846222

03 20

4.10041029246808

6

Table 5: Distance between test image and training images.

The graph shows the distance found between the test

images and training images. The distance is found by using

the Euclidean distance method.

Fig. 14: shows the graph plot distance found with test image

verses training images

E. Ear recognition using PCA

The possibility of identifying people by the shape of their

outer ear was first discovered by the French criminologist

Bertillon, and refined by the American police officer

Iannarelli, who proposed a first ear recognition system based

on only seven features. The detailed structure of the ear is not

only unique, but also permanent, as the appearance of the ear

does not change over the course of a human life [12].

Additionally, the acquisition of ear images does not

necessarily require a person's cooperation but is nevertheless

considered to be non-intrusive by most people. Ears have

gained attention in biometrics due to the robustness of the ear

shape. The shape does not change due to emotion as the face

does, and the ear is relatively constant over most of a person’s

life [13]. It has been suggested that the shape of the ear and

the structure of the cartilaginous tissue of the pinna are

distinctive. Matching the distance of salient points on the

pinna from landmark location of the ear is the suggested

method of recognition in this case.

Ears have several advantages: reduced spatial

resolution, a more uniform distribution of color, and less

variability with expressions and orientation of the face. In

face recognition there can be problems with e.g. changing

lightning, and different head positions of the person. There

are same kinds of problems with the ear, but the image of the

ear is smaller than the image of the face, which can be an

advantage. In practice ear biometrics aren’t used very often.

There are only some cases in the crime investigation area

where the earmarks are used as evidence in court. However,

it is still inconclusive if the ears of all people are unique.

There are some researches done which favor that ear

uniqueness is good enough h[14]. The most interesting parts

Integrated Biometrics (Face, Fingerprint, Iris, Palmprint & Ear) using PCA

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of the ear are the outer ear and ear lope, but the whole ear

structure and shape is used.

In this paper, I have used principal component

analysis, also known as “eigenfaces”, which is a

dimensionality-reduction technique in which variation in the

dataset is preserved. The classification is done in eigenspace,

which is a lower dimension space defined by principal

components or the eigenvectors of the data set.

I have taken IIT Delhi Ear Database version 1.0,

This is touch less ear image database mainly. All the images

are acquired from a distance (touch less) using simple

imaging setup and the imaging is performed in the indoor

environment. The currently available database is acquired

from the 121 different subjects and each subject has at least

three ear images. All the subjects in the database are in the

age group 14-58 years. The database of 471 images has been

sequentially numbered for every user with an integer

identification/number. The resolution of these images is (272

x 204) pixels and all these images are available in jpeg format.

In the experimental result, I have used 10 individuals with

different 3 ear images. So, I have 30 training face images and

20 test images (10 individuals with different 2 ear images).

By using PCA, I reduced the dimensions from (272 x 204) to

(100 x 100). First largest 21 eigen values are selected for 21

eigenear instead of 30 eigenear. The proposed matlab

program works in jpeg format images The Euclidean distance

is used to find out the distance between the test image and

training images. The training image which has the minimum

distance with the test image is supposed to be an ear with

perfect match.

I have applied the MATLAB program on 20 test

images, and all are recognized with exact ear match. One

result with proper match is shown below:

Fig. 15:

The left image is the test image and it is recognized

by the right side image from the training images.

Table showing the distance between test image and

all training images. The test image has the minimum distance

with image 8 by minimum distance 0.7625512875712156.

Imag

es

Minimum

Distance

Imag

es

Minimum

Distance

1 3.6415662325106

113 16

2.0522029700881

63

2 3.5279409023380

35 17

2.3192799508064

272

3 3.5713602457418

32 18

2.7439082290819

865

4 3.6642828670141

91 19

2.2317384315180

53

5 3.5903777541068

087 20

2.7437715888705

645

6 3.4734443779222

595 21

2.6639559952505

483

7 1.0825819993289

152 22

2.1621744146398

667

8 0.7625512875712

156 23

2.1134032073819

453

9 1.2458929317615

064 24

2.5232900947231

03

10 3.0159897078665

100 25

2.8456270277603

49

11 3.2116148934801

75 26

2.8747147788553

43

12 3.2226047212110

234 27

2.4855933107944

93

13 2.8189486366466

703 28

3.3216860730778

21

14 2.6627780720555

716 29

3.5037337925125

04

15 2.7557690319927

51 30

2.7363496713997

04

Table 6: Distance between test image and training images.

The graph shows the distance found between the test

images and training images. The distance is found by using

the Euclidean distance method.

Fig. 16: shows the graph plot distance found with test image

verses training images

VI. RESULT / CONCLUSION

In this paper, I have used PCA algorithm for all biometric

traits such as face, iris, fingerprint, palm print and ear

recognition. The same MATLAB software program is used

for all biometrics. The results are obtained are discussed

earlier. A short format of result is shown here of all biometric

traits.

Integrated Biometrics (Face, Fingerprint, Iris, Palmprint & Ear) using PCA

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Bio

metric

Traits

using

(PCA)

Image

Format

Format

Changed

To

Images

Dimensions

(pixels)

Dimensions Reduced

To

(pixels)

Matched

image

number

with

(M.D)

Minimum

Distance

Found with training

images

FACE Jpeg Jpeg (896 x 592) (56 x 56) 2 1.619653077917682

FINGER Png Jpeg (248 x 338) (100 x 100) 2 2.889597219692923

IRIS bitmap Jpeg (320 x 240) (56 x 56) 6 0.5912620231613743

PALM tif Jpeg (230 x 260) (100 x 100) 14 1.0786326202370273

EAR Jpeg Jpeg (272 x 204) (100 x 100) 8 0.7625512875712156

Table 7: shows the Short Description of the Result Obtained of all Biometric Traits

From the experimental results mentioned above, it

has been proven that PCA is well suited for all biometrics and

given better results. All test images (face, ear, iris, fingerprint,

palmprint) found with perfect match in the training images.

Main reason for using PCA is to reduce the computational

complexity and reduce the large dimensionality of the data

space to the smaller intrinsic dimensionality of feature space.

PCA based method is computationally inexpensive and

requires less time and therefore we considered the algorithm

of PCA if the noise is not strong. The aim of working on the

PCA is to develop a system with increased speed and

accuracy. In Comparison with other methods, our algorithm

can be implemented without using hard mathematical

computations.

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