International Conference on Advances in Engineering & Technology Research (ICAETR - 2014),
IEEE
August 01-02,2014, Dr. Virendra Swarup Group of Institutions, Unnao, India
"Feature Level Fusion of palm print and Fingerprint Modalities using Discrete Cosine Transform" Mr.
Mr
Aditya Gupta
Mr. Mahesh Vaidya
Ekjok Walia
Electronics & Telecommunicatio
Electronics & Telecommunication
Electronics & Telecommunication
College ofEngineering Pune
Siv Nadar.University
aditvagupta2590(wgmail.com
Multimodal Biometric
system,
Discrete Cosine
Tran�orm (DCT), Standard deviation.
I.
for
person's
or
identification
can't
be
considered to be reliable. Traditional authentication systems could not identifY whether user is fake or authenticated. All these
traditional
authentication
system can be
stolen or
spoofed. Using biometric trait of a person is better option one as they are less venerable to theft or spoof attacks. Every
person
has
different
biometrics.
Commonly
used
biometric traits are face, fingerprint, iris, palm print, palm veins etc. [I].
However uni-modal biometric system suffers
from noisy data, non-universal acceptability. Hence, it may not be able to achieve desired results in actual applications. The main drawback of single modal biometric system is that they are less immune to spoof attack. Solution to this is to go for
multimodal
biometric system. A
multimodal biometric
system can be made by fusing two uni-modal system. This is more secure.
Also using multi modal biometric,
FAR is
reduced. This in tum makes systems far better in terms of providing schemes
accurate
authentication.
Minutia base techniques for fmgerprint feature extraction are one of basic technique used for fingerprint recognition [I]. But in this technique there is problem of proper alignment, which is difficult to get. Amit Deshmukh, [8] proposed
However,
multimodal
introduce increment in number of computations
required at different stages of authentication. A lot of research has been done on multimodal techniques in order to counter problems arising in uni-modal systems, so that they can meet required performance levels. Multimodal biometric fusion can be done using Fusion at the
banks to get directional information for feature extraction. But it is complex and requires large memory space to store filter coefficients. M.P.Dale proposed [17] DCT based fingerprint recognition having good recognition rate. As fingerprint is rich in high frequency content, this feature can be exploited. Using
B. For Palm print Principle Component Analysis (PCA) is used for recognition of palm prints and palm vein The features extracted by PCA are best description of the data, but not the best discriminant features.
print and fmgerprint biometric modalities was proposed by
Deshpande [9] which performs better than uni-modal systems. Thus it will be more appropriate to go for feature base fusion techniques. The efficiency of any algorithm depends on the steps that have been undertaken for feature extraction and for
Fisher
Linear
Discriminant
(FLD)
can be used
instead of PCA. The major drawback of applying FLD is that it may encounter the small- sample-size problem. ling Liu and Yue
Zhang[19]
introduce
2DFLD
that
computes
the
covariance's matrices in a subspace of input space and achieved optimal discriminate vectors. This method gives greater
recognition
complexity
but
is
accuracy
with reduced
computational
difficult
to
on
implement
hardware
[5].David Zhang extracted texture features from low resolution palm print images, based on 2D Gabor phase coding scheme. [7].But Gabor is quite complex and time taking as well. Using minutiae extraction for fingerprint image, local binary pattern for extracting features of palm print and combining using wavelet
based
fusion
technique
outperforms
individual
modalities being used for recognition. This is sensitive to varying rotation and translation [6].We can also go for Fourier transform [11] but there calculations are more. We can go for DCT to extract information present in frequency domain and to utilize it during recognition stage.
Feature Extraction Level, Fusion at the decision Level and Fusion using score matching. A feature level fusion of palm
discrimination.
Gabor filter
bank technique for feature extraction. This uses Gabor filter
higher frequency can be extracted.
INTRODUCTION
verification
A. For Fingerprint
high frequency DCT coefficients features corresponding to
Traditional authentication systems using ID card or passwords etc.
[email protected] m
ekjotwaliaiaJhotmail.com
Abstract-Biometric systems have become a major part of research due its application of identification. Paper proposes a multimodal biometric system using palm prints modality combined with fingerprint modality. The proposed methodology uses standard deviation of pre-defined block of DCT coefficient as feature vector. Recognition process is being done by performing distance measurement between feature vector of testing and training data set. Results show that the False Acceptance Rate (FAR) of feature level fusion is less than that of uni-modal systems, hence having multimodality is advantageous. Testing and training is done on database of 150 students of College of Engineering Pune. Keyword�
Noida, India
Delhi, India
Pune, India
Paper
proposes
a
fusion
level
multimodal
biometric
system, which uses palm print with figure print modalities. Any
input
modality
is
resized
to
fixed
size
(64x64).
Fingerprint and palm print image is resized to 64x64. DCT is taken. Standard deviation is calculated for different predefme block of DCT coefficients. Standard deviation is stored as feature vector. 39 blocks were taken for palm print and 19
978-1-4799-6393-5/14/$31.00 ©2014 IEEE
International Conference on Advances in Engineering & Technology Research (ICAETR - 2014),
IEEE
August 01-02,2014, Dr. Virendra Swarup Group of Institutions, Unnao, India
blocks for fmgerprint. These standard deviations are stored in
block of DCT coefficient corresponds to higher and lower
the form of single vector. The feature vector for every input is
frequency were taken as shown in Fig.4.
independent of the feature vector of any other input, even
blocks for fmgerprint.
though it of the same person. All such independent feature
Fingerprint
vectors are used for the recognition purpose. The paper is organized Cosine
as
follows. Section II reminds
Transform
.Section
III
about Discrete
describes
We have taken 39
modality
Palm
proposed
print
methodology for multimodal biometric feature based fusion. Section IV sunnnarizes the results for the proposed system Finally, section V is dedicated to conclusion and remarks which have been made from results. II.
DISCRETE COSINE TRANSFORM (OCT)
hnages contain large amount of data so to
store
them
compression is required. DCT is a compression technique. Firstly an entire image is divided into blocks of size S x S.
Feature
Feature
extraction
extraction
using standard
Using standard
deviation of DCT
deviation of DCT
Each block is transformed independently by using a 20-0CT.
[ (2X+ l)iJr ] [ (2Y+ l)jJr ] D(i,j)= �C(i)C(j)I Ip(x,y)cos
Feature
OCT for pixel at location (i, j) is given by equation:
1
V-I
,, 2 1\
x=O
fusion
V-I
2j\iT
y=O
COS
2j\iT
(1)
Where, c(u) is given by:
c(u) =
{_I
if u=o
J2 1
}
Distance
Template
measure
database
(2)
ifu>O
for SXS block it is given as:
Fig. I Flow Chart of proposed system
Standard deviation can be calculated using formula as given The OCT is applied to each block from top to bottom Energy is
compacted
in
few
coefficients.
Lower
below:
frequency
components exhibit more energy. With OCT image can be retained with very less number of coefficients without much loss of subjective quality.
III.
(4)
PROPOSED METHEDOL OGY
Fig. I shows the block diagram of proposed system The main aim of the proposed methodology is to reduce the FAR of the system We have taken images of palm print and finger print.
Where, Xi X
n
�
=
=
/h value of data. Mean value.
no of data points
We have resized all the images to dimension 64x64. After that image is being converted to gray scale. OCT have been taken
Pre-Processing (For palm print)
out of that gray scale image. For palm print preprocessing id
Image alignment is done to find out the key points. Using key
donr which is explained latter. For feature extraction blocks of
point find out region of interest. To find out key points images
DCT coefficient have taken and their standard deviation is
of hand containing palm print and palm veins is first binarized
calculated and stored as feature vector. As fmgerprint is rich in
as shown in fig 2. Key points have been found out from those
high frequency content that's why block of DCT coefficient
binarized images. After Cropping ROI from palm veins and
corresponds to higher frequency were taken as shown in Fig.3.
palm print the image is resized and then OCT coefficients are
We have taken 19 blocks for fmgerprint. Similarly palm print
calculated.
is rich in high as well as low frequency content that's why
978-1-4799-6393-5/14/$31.00 ©2014 IEEE
IEEE
International Conference on Advances in Engineering & Technology Research (ICAETR - 2014), August 01-02,2014, Dr. Virendra Swarup Group of Institutions, Unnao, India
8
o
16
32
24
40
48
MS I M7
I 1M2 (b)
. (a)
(c)
b
� (d)
M6 f--
(c) Key point detection
Fig.2 (a) Original (b) Binarized (d) Cropped ROI for palm print
Mi
M14 I I M16
I 1M3
MU
I I MiS
Mll Mll
MiO
M12
M8
64
56
M17
M9
M19
Acquisition (palm Print and Palm vein)
FigA DCT coefficent selection (M I-Ml 9)from image(64x64) for fingerprint modality.
In this JAI AO-080-GE camera is used to capture NIR hand
Euclidean
vein images. The camera contains two II3" progressive scan
difference of each feature vector of test and database image is
CCO with 1024x768 active pixels, one of the two CCO's is used to capture visible light images (400 to 700 nm), while the
Distance
[18]:
In
Euclidean
distance
metric
squared which increases the divergence between the test and database image if the dissimilarity is more.
other captures light in the NIR band of the spectrum (700 to 1000nm). For fingerprint device name as Finger Key were used. This consists of optical sensor. We can train the system even usig single image of input modality. Even if there is new entry so
d(x,y) = �::Ix} - y;1 N
2
(5)
}=1
we just need to calculate standard deviation of different blocks and store them in form of feature block. In figure 4 MI-MI9 tells the block whose standard deviation had been taken. 8
o
�
M21
M7
M3 2
24
16 M4
M8
M5
I I M22
32
40
M17 M16
1
M36 M18
M19
M23 M20
M35 M31
1 I
64
M28
M6 M9
56
48
Canberra
Distance
[18]:
Canberra
distance
is similar to
Manhattan distance. The distinction is that the absolute difference between the variables of the two objects is divided by sum of absolute variable values prior to summing.
M32
(6)
M37 M24
1
M33 M38
M26
1
M27
I
M34
M25
M14
IV.
M30
RESULTS
M15 M29
f-f-M13
Ml0
M12
The methods presented above were tested on data base of 150 students of College of Engineering Pune for figure print and palm print. For testing, we have taken 4 images for training and 2 images for testing purpose. Total we have taken 150
Fig.3
Fig.4.16
DCT
coefficent
selection
(MI-M38)from
image(64x 64 ) for palm print modality
subjects
.The
fusion process is being carried out using
OpenCV 2.4 .The computer used to carry out these tests is an Intel 1-3 with 2.2 GHZ and 2 of RAM.
The matching score between the test and the training database
Table I shows res ult of FAR, FRR and GAR for palm print
is calculated as the distance between their features vectors. A
and
smaller distance score means a better match. For the proposed
Identification test was carried out for all 150 subjects and
work, we use Euclidean distance (01) and Canberra distance
following recognition rates were obtained. While obtained
(02) for distance measurement.
above parameters the threshold was kept constant throughout.
fmgerprint
modality
978-1-4799-6393-5/14/$31.00 ©2014 IEEE
when
taken
individually.
IEEE
International Conference on Advances in Engineering & Technology Research (ICAETR - 2014), August 01-02,2014, Dr. Virendra Swarup Group of Institutions, Unnao, India
With Euclidean distance (D2) measure GAR of 97.0% for
Euclidean distance measure GAR of 99.0% was obtained by
palm
the feature level fusion of palm print and fmgerprint.
print
modality
modality
were
and
GAR of
obtained.
With
98.0%
Canberra
for
fmgerprint
distance
Canberra distance measure the GAR of 99.33% using fusion
measure the GAR of 98.33% for palm print modality was
technique which is better than uni modality system. It can be
obtained. However, GAR of fmgerprint unchanged to 99.0%.
observed that, for Canberra distance the FAR of .67% is
Table I sholMl result for FAR, FRR GAR for faoe and fingetprint as uni-modal biometric trait.
Modality
I
With
(Dl)
FRR(%)
FAR (%)
GAR(%)
Dl
D2
D1
D2
D1
D2
Palm prmt
.67
1.67
1.67
3
98.33
97
Fingerprint
.67
.67
1
2
99
98
Fig.5 shows graph of FAR and FRR for palm print and fingerprint modality when taken as uni-modal system. The graph was made for the results obtained against Canberra
achieved when fusion was taken. This FAR was lower than uni-modality
system.
The
result
shows
improvement
in
recognition rate as compare to previous work done on fusion of same modality [16]. Table 11 sholMl result for FAR, FRR GAR for palm print and fingerprint fusion.
Modality
Fusion of Palm print and Fingerprint
FRR(%)
FAR (%)
GAR(%)
D1
D2
D1
D2
D1
D2
.67
1
.67
1
99.33
99
distance. palm print
(150 person using Canberra Distance)
Fig.6 shows graph of recognition rate vs. number of subjects for fusion of face and fingerprint modality. The graph was made for the results obtained against Euclidean distance.
"*
2.5
I 2 0:
11: '" 1. � 5
Recognition Rate for
:e-
� 1 o:
Fusion technique
0.5 g5
3.55
3.6
3.7
3.65
3.75
Threshold
3.8
3.85
3.9
3.95
4
100 99.8
(a) finger print
100.2
(150 person using Canberra Distance)
16
99.6 99.4
14
\ \ \
-
Recognitio n Rate for
\
Fusion
99.2
technique
99 98.8
Fig. 6 Recognition rates vs. number of subjects in the database
°lL_1.L. . C2 5==1� . 3= 1.�35�1':. 4::::1.'C :: 45::;j1.5 0 5 -1L..1 -1.L.15-1.L.2=1.
taken v.
Threshold
(b)
CONCLUSION
We have proposed a feature level fusion of palm print and
Fig. 5 Graph for FAR and FRR (a) for palm print,
fmgerprint
modalities
using
DCT
for
feature
extraction.
Comparison of recognition rate for face and figure modalities
(b) Fingerprint
had been made using different distances. Results show that the
Table II shows result of FAR, FRR and GAR for fusion of
accuracy of fmgerprint and palm print is similar. Results prove
palm print and fingerprint modality. While obtaining above
that the proposed methodology has less FAR as compared to
parameters the threshold was kept constant throughout. With
uni-modal biometric system. This proves the robustness of
978-1-4799-6393-5/14/$31.00 ©2014 IEEE
IEEE
International Conference on Advances in Engineering & Technology Research (ICAETR - 2014), August 01-02,2014, Dr. Virendra Swarup Group of Institutions, Unnao, India
system towards spoof attack on comparison with uni-modality system As compare to Euclidean distance Canberra distance shows better result for proposed methodology.
System is
adaptive to new entry of subject. Each image of palm print and
[14] J. You, W.K. Kong. D. Zhang. K.H. Cheung. "On hierarchical palmprint coding with multiple features for personal identification in large databases", IEEE Transactions on Circuits and Systems for Video Technology 14 (2) (2004) 234-243.
fmgerprint modality is considered to be independent, hence
[15] Chengjun Liu, and Harry Wechsler," Gabor Feature Based Classification
system can be trained using single training set. Proposed
Using the Enhanced Fisher Linear Discriminant Model for Face Recognition", IEEE Transactions On Tmage Processing, Vol. II, No. 4, Apri12002, pp. 467476.
methodology
uses
single
feature
vector
of
dimension
lx19(fmger) corresponding to single frame. Hence proposed methodology requires lesser memory. System is easy to design also. Also the results shows better recognition rate as compare to previous work[16]. REFERENCES
[16] V. D. Mhaske, A. 1. Patankar,"Multimodal biometrics by integrating fingerprint and Palmprint for Security", International Conference on Computational Tntelligence and Computing Research, IEEE-20 13 .
[1] Anil K. Jain, Patrick Flynn, ArunA. Ross, "Hand book of Biometrics", © 2008 Springer Science+Business Media.
[17] M. P. Dale, M. A. Joshi, M. K. Sahu, "DCT Feature Based Fingerprint Recognition",IEEE International Conference on Intelligent And Advanced Systems (TCTAS 07), 25i> to 28th Nov. 2007, Malaysia, Page(s):611 - 615.
[2] Suznki, S. and Abe, K., "Topological Structural Analysis of Digitized Binary Tmages by Border Following", CVGTP 30I, pp3 246
[18]Sung-Hyuk Cha, "Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions", Tnternational Journal of
[3 ] D. Vaidya, S. Paw.lr, Dr. M. A. Joshi, "Feature-level Fusion of PalmPrint and Palm Vein for Person Authentication Based on Entropy Technique", lJECT Vo!' 5, Issue Spl- l , Jan - March 2014
Mathematical Models and Methods in Applied Sciences, Issue 4, Volume I, 2007, pp. 153 -157.
[4]. c.-L. Lin, K.C. Fan,"Biometric verification using thermal images of palm-dorsa vein patterns", IEEE Trans. Circuits Syst. Video Techno!., Vo!' 14, No. 2, pp. 199-213 , Feb.2004. [5] Jing Lin, Yue Zhang, "Palm-Dorsa Vein Recognition Based on TWl Dimensional Fisher Linear Discriminant", TEEE,20II [6] David Zhang, "Online palm print identification" [7] Ajay Kumar, Venkata Prathyusha, "Personal Authentication Using Hand Vein Triangulation and Knuckle Shape", IEEE transactions on Image Processing, VOL. 18, NO. 9, September 2009. [8] Amit Deshmukh, Sheetal Paw.lr , Dr Madhuri joshi , "Feature level Fusion of Face and Fingure Print Modalities using Gabor Filter Bank",2nd International conference on signal processing computing and control.lEEE2013 . [9] Deshpande, A., S., Patil, S., M., Lathi, R. 2012. A MuItimodel Biometric Recognition System based on Fusion of Palmprint Fingerprint and Face. Tnternational Journal of Electronics and Computer Science Engineering. TSSN-2277-1956.
[10] Hyunjong Cho and Seungbin Moon , Department of Computer Engineering, Sejong University, Seoul, Korea," Comparison of PCA and LDA based face recognition algorithms ,under illumination variations", ICROS SICE International Joint Conference 2009. [11] W. Li, D. Zhang. Z. Xu. 2002. Palmprint identification by Fourier transform. International Journal of Pattern Recognition and Artiticial Intelligence 16 (4) (2002) 417-432. [12] Ephin M ,"Survey on MuItimodal Biometric using Palm print and Fingerprint", AICWlC-20 13. [13 ] Joni-Kristian Kamarainen, Ville Kyrki, Member, IEEE, and Heikki Kalviainen, Member, IEEE Invariance Properties of Gabor Filter-Based Features-Overview and Applications, IEEE Transaction On Tmage Processing, Vo!' IS, NO. 5, MAY 2006.
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