The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.

1. The International Journal of Engineering And Science (IJES)
||Volume||1 ||Issue|| 2 ||Pages|| 248-252 ||2012||
ISSN: 2319 – 1813 ISBN: 2319 – 1805
Extraction of Dynamic Region of Interest (ROI) for Palmprint
using Templates Databases
Mr. P.Srinivas 1 Mrs. Y.L. Malathilatha2 Dr. M.V.N.K Prasad 3
1. Associate Professor, CSE Department, Geethanjali College of Engineering & Te chnology(GCET), Hyderabad, A.P.
2. Associate Professor, CSE Department, Swami Vivekananda, Institute of Technology (SVIT), Hyderabad, A.P.
3. Assistant Professor, Institute of Development and Research in Banki ng Technology (IDRBT), Hyderabad, A.P.
----------------------------------------------------------------Abstract-----------------------------------------------------------
Bio metric recognition predicated on palm-print features contains different processing stages such as data
acquisition, pre-processing, feature extraction and matching. This paper fixates on the pre-processing section
which is quite important in providing high accuracy in pattern recognition. Preprocessing is utilized to align
different palmprint images and to segment the central part for feature ext raction. In this paper we imp lement a
method of Dynamic Region Of Interest depending on the size of the image. Most of the existing work uses static
regions fro m palm print, not utilizing significant portion of the palm. Intuitively, the more area utilized for
feature extraction and matching, the better the recognition use of templates databases.
Keywords: Palmprint, Reg ion of Interest (ROI), Wrin kles.
---------------------------------------------------------------------------------------------------------------------------------------
Date of Submission: 11, December, 2012 Date of Publication: 25, December 2012
----------------------------------------------------------------------------------------------------------------------------- ----------
I. Introduction
Bio metrics is considered to be one of the steps 1) Binarzing the palm image 2)
robust, reliable, efficient, utilizer-amicable, secure Extracting the shape of the hand or palm 3)
mechanis ms in the present automated world. Detecting the key point 4) Establishing a
Bio metrics can provide security to a wide variety coordinate system and 5) Ext racting the ROI. Most
of applications including secure access to of the research uses Otsu‟s method for binarizing
buildings, computer systems, laptops, cellular the hand image [1]. Otsu‟s method calculates the
phones and ATMs. Fingerprints, Iris, Vo ice, Face, suitable global threshold value for every hand
and palmp rint are the different physiological image. According to the variances between two
characteristics utilized for identifying an classes, one of the classes is the background while
individual. Palmprint verificat ion system utilizing the other one is the hand image. The boundary
biometrics is one of the emerging technologies, pixels of the hand image are traced utilizing
which recognizes a person predicated on the boundary tracking algorith m [2]. The key points
principle lines, wrinkles and ridges on the surface between fingers are detected utilizing several
of the palm. These line structures are stable and different implementations including tangent [3],
remain unchanged throughout the life of an Bisector [4], [5] and Finger predicated [6], [7].
individual. More importantly, no two palmp rints
fro m different individuals are the same, and The tangent predicated approach
normally people do not feel uneasy to have their considers the edges of two finger holes on the
palmprint images taken for testing. Therefore binary image wh ich are to be traced and the
palmprint predicated recognition is considered prevalent tangent of two fingers holes is found to
both utilize- amicable as well as fairly accurate be axis X. The middle po int of the two tangent
biometric system. points is defined as the key points for establishing
Bio metric recognition predicated on the coordinate system [3]. Bisector predicated
palm-print features contains different processing approach concentrates on not joining the fingers by
stages such as data acquisition, pre-processing, converting the upper region of the fingers and the
feature ext raction and matching. This paper fixates lower component of the image to white. It aims in
on the pre-processing section which is quite determining two centroids of each finger gaps for
important in providing high accuracy in pattern the image alignment since only the centre of
recognition. Preprocessing is utilized to align gravities within the defined three finger gap
different palmprint images and to segment the region. After locating the three finger gaps the
central part for feature extraction. Most of the centre of gravity of the gaps can be determined.
preprocessing involves generally five prevalent Then the two centroids of each finger gap are
connected to obtain the three lines. The line drawn
www.theijes.com The IJES Page 248

2. Extraction of Dynamic Region of Interest (ROI) for Palmprint using Templates Databases
through the centroids of each finger gap region
intersects the palm of a key point and the points to 2.1 Location of figure web points
setup a coordinate system [4]. All these The follo wing processes are performed to
approaches utilize only the information on the locate finger web locations using binary palmprint
boundaries of fingers. While Ku mar et al proposes images.
to utilize all informat ion in palm [8] they fit an
ellipse to a binary palmprint image. According to 1. Image is converted to binary with grey value 0
orientation of ellipse, a coordinates system is or 1.
established. Most of the preprocessing algorithm 2. Boundary tracing 8-connected pixels algorith m
segments square regions for feature extraction, but is applied on the binary image to find the boundary
some of them segment circular [9] and half of palmprint image. The starting point is the
elliptical reg ions [10]. bottom left point “Ps” as shown in figure 2 and the
tracing direction is counter clockwise. The end
Generally there are t wo kind of images point is also “Ps”. And these boundary pixels are
utilized in palm-p rint recognition: Online and collected in Boundary pixel vector (BPV).
Offline. On line images are those taken with digital 3. Euclidean distance is calculated between BPV
cameras or scanners. Offline ones are those and Ps with formu la
produced by ink on paper [11]. The database we DE (i) = (Xp − Xb (i) + (Yp – Yb (i))
utilize for testing our method is PolyU [12] that (1)
utilizes online images. The images in this database where ( Xp , Yp ) are the X and Y co-ord inates of
are low-resolution ones and are suitable for real- the Ps ( Xb(i), Yb(i) ) is the co-ordinate of the
time application testing. A sample of the images border pixel, and DE (i) is the Euclid ian distance
fro m database is shown in Figure 1. between Ps and Ith border pixel. A Distance
distribution diagram shown in figure 3 is
The rest of this paper is organized as constructed using the vector DE. The constructed
follows: Section 2 prov ides proposed Dynamic diagram pattern is similar to geometric shape of
ROI ext raction method. Section 3 discusses the the palm. In the figure 3, three local minima and
experimental results. Finally Conclusions are four local maxima can be visually perceived which
presented in section 4. resembles the four-finger tips (local axima) and
four finger webs (local min ima) i.e. valley between
fingers.
4. The first and the third finger web point is taken
and the slope joining this two lines is calculated
utilizing formu la
tan α =Y/X, (2)
where Y= y 1-y 3, X= x1-x3, (x1, y 1) & (x3, y 3)
are the co-ordinates of FW1 & FW3 finger web
point respectively, α is the slope of the line.
Figure 1: Image of Poly U database
Table 1: Notation used in this paper
FW Figure web point
X x-coordinate of boundary pixels
Y y- coordinate of boundary pixels
Xb x-coordinate of border p ixel
Yb y-coordinate of border pixel Figure 2: Boundary pixels of palm image
Ps Starting point in the image
Xp x-coordinate of P
Yp y-coordinate of P
II. 2. Proposed Methodology For Palm
Extraction Figure 3 : Distance distribution diagram
Image prepossessing is conventionally the 2.2 Dynamic ROI Extraction
first and essential step in pattern recognition. In The following steps are performed to ext ract the
this paper a Method [13] is adopted which uses ROI.
finger webs as the datum points to develop an 1. The image is then rotated at an angle α to align
approximate Region OF Interest to which changes the straight line joining FW3(x3, y3) & FW1(x1,
are made to surmount the limitations of existing y1) with the horizontal axis as shown in figure 4.
method.
www.theijes.com The IJES Page 249

3. Extraction of Dynamic Region of Interest (ROI) for Palmprint using Templates Databases
Figure 8: Boundary of Binary image N plotted
Figure 4: Image Q after rotation with finger web using b1 & b2 matrices
point
2. A fter rotation, we reiterate step 1 to 5 of section
2.2 are applied to get finger web points of the
rotated image as the co-ordinates of finger web
points changes after rotation. The finger webs
after rotation are named as FR1, FR2 and FR3.
3. Now boundary tracing algorith m is applied on 4. For Width: The maximu m Y-coordinate in the
the binary image figure 5 and X & Y co-ord inates b2- mat rix is calculated using (3)
of all the boundary pixels are stored in different Ym=max (b2)-k
matrices. X co-ordinate values of boundary pixels (3)
are stored in b1-matrix and Y co-ordinate values where k=15 is chosen empirically for
are stored in b2-matrix. Plots between b1-matrix experimental purpose. Then for th is new Ym there
(X-co-ordinate) and boundary pixels and b2-matrix will be two X coordinates (say X1 and X2) on the
and boundary pixels is shown in figure 6 and boundary as shown in figure 9 and can be found
figure 7 respectively. The boundary of a binary fro m matrix b 1 wh ich is show in the figure 10a
image obtained by drawing a plot between b1- and 10b. Now width of ROI is calculated using (4).
matrix and b2-matix and shown in figure 8. as shown in
W idth = abs(X1-X2)
(4)
Figure 5: Binary Image Dimension
Figure 9: Plotting X1 & X2 on boundary plot and
inverted
Figure 6 : Plot of X co -ordinates (b1-matrix)
(a)
against the boundary pixels
(b)
Figure 10: a) Max Y-Coordinate and b) X1 and
Figure 7 : Plot of Y co-ord inates (b2-matrix)
X2 values
against the boundary pixels
For Height: To calculate the height we require
maximu m Y-coordinate (Ymax) and minimu m Y-
coordinate(Ymin ).The Ymax can be calculated
utilizing (5) by subtracting it fro m P which is the
length of the image.
www.theijes.com The IJES Page 250

4. Extraction of Dynamic Region of Interest (ROI) for Palmprint using Templates Databases
demonstrates that fine-tuned size ROI cover
1. diminutively minuscule area and valued
Ymax=P-Ym (5) informat ion is missed where as dynamic size ROI
extracts maximu m size ROI and 99.9% ROIs
The Ymin can be calcu lated utilizing (6) by find without background information.
the minimu m Y-coordinate out of all three web The ROI images we obtained fro m each
points after complementing it with the length of palm image had maximu m Size of ROI 201* 174
image. and minimu m Size of ROI 137*163 shown in
figure 13.
Ymin = min( P-y1,P-y2 ,P-y 3) (6)
where y1,y2 and y3 are y-coordinate of web points
FR1,FR2 and FR3 respectively.
Height is the distinguishment between Ymax and
Ymin and is calculated utilizing (7) shown in
figure 11.
Height=abs(Ymax-Ymin ) (7)
Figure 11: For the lo west left most point of
rectangle
Palmp rint Image Size of ROI 201 * 174
6. We have calculated height and width of palm
print image. Now, to get maximu m ROI Square (a)
region we require top left most point and lowest
right most point, vividly it will be (X1, Ym) right
most points and (X1, P-Ym) as the lowest leftmost
point. The Dynamic ROI extracted is shown in
figures 12.
Figure 12: Palmp rint Images and corresponding
Dynamic ROI Extracted
III. Experi mental Result
We experimented our approach on Hong
Kong Polytechnic University Palmprint database
Palmp rint Image Size of ROI 137 * 163
[12].The database was acquired at Hong Kong
Polytechnic University (Ch ina) utilizing camera. (b)
In its current version the database contains, Figure 13: a) Palmprint Images and corresponding
Maximu m size Dynamic ROI Ext racted (201 *
7752(8-b it) grey-scale images corresponding to
386 subjects. The experiment has been performed 174)
on a system of 2.0GHz CPU and 256 MB of RAM. b) Palmp rint Images and corresponding
Minimu m size Dynamic ROI Ext racted (137 *
Most of the researchers [13-18] ut ilized the PolyU
Palmp rint database [12] and they ext racted fine- 163)
tuned size 128* 128 ROI. Result of the proposed
Algorith m are co mpared with fine-tuned size ROI
extraction Algorithm[13].The experiment
www.theijes.com The IJES Page 251

5. Extraction of Dynamic Region of Interest (ROI) for Palmprint using Templates Databases
VI. Conclusions international conference on audio and video
Palm segmentation is the key step in based biometric person authentication, pp.
palmprint recognition system. Seg mentation of 668-678, 2003.
palm includes separation of palm which is in [9] A. Kumar & D.Zhang “Integrating Shape
between the wrist and fingers of hand images. In and texture for hand verification” in
this paper, we propose a Dynamic ROI extract ion Proceedings of Third International
technique depending upon the size of the image. Conference on Image & Graph ics, pp. 222-
The proposed method extracts maximu m possible 225, 2004.
ROI region without background informat ion when [10] C. Poon.D.C.M. Wong and H.C. Shen “ A
compared to the existing fixed ROI extract ing New Method in Locating and Segmenting
techniques [13-18] .We found that the efficiency palmprint into region-of-interest”, in
of our proposed approach agrees with the other proceedings of the 17th International
systems in the state of art and is better for the Conference on Pattern Recognition, vol. 4,
future feature extract ion and matching. pp. 533-536, 2004
[11] D. Zhang, W. Shu, “Two Novel
References Characteristics in Palmp rint verification:
[1] J.S. Noh, K.H. Rhee, “Palmprint datum point invariance and line matching”,
identification algorith m using Hu invariant Pattern Recogn., vol. 32 pp. 691-702, 1999.
mo ments and Otsu binarization”, in: [12] The Hong Kong Polytechnic University
Proceeding of Fourth Annual ACIS PolyU Palmprint Database,
International Conference on Computer and www.co mp.edu.hk/bio metrics .
Information Science, 2005, pp. 94–99. [13] Lin, C-L., Chaung, T.C. and Fan, K-C.
[2] J. Doublet, M. Revenu, O. Lepetit, “Robust (2005) „Palmp rint verification using
grayscale distribution estimat ion for hierarchical deco mposition‟, Pattern
contactless palmprint recognition”, in: Recognition, Vo l. 38, No. 12, pp.2639–
Proceedings of the First IEEE International 2652(2005).
Conference on Bio metrics: Theory, [14] Chen, J., Moon, Y., Wong, M., Su, G.:
Applications, and Systems, 2007, pp. 1–6. Palmp rint authentication using a symbolic
[3] D. Zhang, W.K. Kong, J. You, M. Wong, representation of images. Image and Vision
“On-line palmp rint identification”, IEEE Co mputing 28, 343– 351 (2010)
Transactions on Pattern Analysis and [15] Huang, D., Jia, W., Zhang, D.: Palmprint
Machine Intelligence 25 (9) (2003) 1041– verification based on principal lines. Pattern
1050. Recognition 41, 1316– 1328 (2008)
[4] W. Li, D. Zhang, Z. Xu, “Palmprint [16] Jia, W., Huang, D., Zhang, D.: Palmprint
identification by Fourier transform”, verification based on robust line orientation
International Journal of Pattern Recognition code. Pattern Recognition 41, 1504–1513
and Artificial Intelligence 16 (4) (2002) (2008)
417–432. [17] Sun, Z., Tan, T., Wang, Y., Li, S.Z.: Ord inal
[5] X. Wu, K. Wang, D. Zhang, “HMMs bas ed Palmp rint Representation for Personal
palmprint identification”, in n Proceedings Identificat ion. In: Proceedings of IEEE
of ICBA. 2004, vol. 3072, 2004, pp. 775– International Conference on Co mputer
781. Vision and Pattern Recognition, vol. 1, pp.
[6] C.C. Han, “A hand-based personal 279–284 (2005)
authentication using a coarse-to-fine [18] Zhang, D., Kanhangad, V., Luo, N., Ku mar,
strategy”, Image and Vision Co mputing 22 A.: Robust palmprint verification using 2D
(11) (2004) 909– 918. and 3D features. Pattern Recognition 43,
[7] C.C. Han, H.L. Cheng, C.L. Lin, K.C. Fan, 358–368 (2010)
“Personal authentication using palm-print [19] CASIA Palmprint Database,
features”, Pattern Recognition 36 (2) (2003) http://www.cbsr.ia.ac.cn/PalmDatabase.htm
371–381. l
[8] A. Ku mar, D.C.M. Wong, H.C. Shan and [20] IIT Delh i Touchless Palmprint Database
A.K. Jain, “Personal verification is using version 1.0,
palmprint & hand geometry biometric”, in http://web.iitd.ac.in/~ajaykr/ Database_Palm
AVBPA 2003, proceedings of 4th .html
www.theijes.com The IJES Page 252