Synthesis and Charaterization of Poly (Flouroaniline)/Mwcnt Composites

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.

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Date of Submission: 11, December, 2012

Date of Publication: 25, December 2012

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I.

Introduction

Bio metrics is considered to be one of the robust, reliable, efficient, utilizer-amicable, secure mechanis ms in the present automated world.

Bio metrics can provide security to a wide variety of applications including secure access to buildings, computer systems, laptops, cellular phones and ATMs. Fingerprints, Iris, Vo ice, Face, and palmp rint are the different physiological characteristics utilized for identifying an individual. Palmprint verificat ion system utilizing biometrics is one of the emerging technologies, which recognizes a person predicated on the principle 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 palmp rints fro m different individuals are the same, and normally people do not feel uneasy to have their palmprint images taken for testing. Therefore palmprint predicated recognition is considered both utilize- amicable as well as fairly accurate biometric system.

Bio metric recognition predicated on palm-print features contains different processing stages such as data acquisition, pre-processing, feature ext raction 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 extraction. Most of the preprocessing involves generally five prevalent

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steps 1) Binarzing the palm image 2) Extracting the shape of the hand or palm 3) Detecting the key point 4) Establishing a coordinate system and 5) Ext racting the ROI. Most of the research uses Otsu‟s method for binarizing the hand image [1]. Otsu‟s method calculates the suitable global threshold value for every hand image. According to the variances between two classes, one of the classes is the background while the other one is the hand image. The boundary pixels of the hand image are traced utilizing boundary tracking algorith m [2]. The key points between fingers are detected utilizing several different implementations including tangent [3], Bisector [4], [5] and Finger predicated [6], [7].

The tangent predicated approach considers the edges of two finger holes on the binary image wh ich are to be traced and the prevalent tangent of two fingers holes is found to be axis X. The middle po int of the two tangent points is defined as the key points for establishing the coordinate system [3]. Bisector predicated approach concentrates on not joining the fingers by converting the upper region of the fingers and the lower component of the image to white. It aims in determining two centroids of each finger gaps for the image alignment since only the centre of gravities within the defined three finger gap region. After locating the three finger gaps the centre of gravity of the gaps can be determined.

Then the two centroids of each finger gap are connected to obtain the three lines. The line drawn

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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 setup a coordinate system [4]. All these approaches utilize only the information on the boundaries of fingers. While Ku mar et al proposes to utilize all informat ion in palm [8] they fit an ellipse to a binary palmprint image. According to orientation of ellipse, a coordinates system is established. Most of the preprocessing algorithm segments square regions for feature extraction, but some of them segment circular [9] and half elliptical reg ions [10].

Generally there are t wo kind of images utilized in palm-p rint recognition: Online and Offline. On line images are those taken with digital cameras or scanners. Offline ones are those produced by ink on paper [11]. The database we utilize for testing our method is PolyU [12] that utilizes online images. The images in this database are low-resolution ones and are suitable for realtime application testing. A sample of the images fro m database is shown in Figure 1.

The rest of this paper is organized as follows: Section 2 prov ides proposed Dynamic ROI ext raction method. Section 3 discusses the experimental results. Finally Conclusions are presented in section 4.

2.1 Location of figure web points The follo wing processes are performed to locate finger web locations using binary palmprint images.

1. Image is converted to binary with grey value 0 or 1.

2. Boundary tracing 8-connected pixels algorith m is applied on the binary image to find the boundary of palmprint image. The starting point is the bottom left point “Ps” as shown in figure 2 and the tracing direction is counter clockwise. The end point is also “Ps”. And these boundary pixels are collected in Boundary pixel vector (BPV).

3. Euclidean distance is calculated between BPV and Ps with formu la DE (i) = (Xp − Xb (i) + (Yp – Yb (i)) (1) where ( Xp , Yp ) are the X and Y co-ord inates of the Ps ( Xb(i), Yb(i) ) is the co-ordinate of the border pixel, and DE (i) is the Euclid ian distance between Ps and Ith border pixel. A Distance distribution diagram shown in figure 3 is constructed using the vector DE. The constructed diagram pattern is similar to geometric shape of the palm. In the figure 3, three local minima and four local maxima can be visually perceived which 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 Ps Starting point in the image Xp x-coordinate of P Yp y-coordinate of P

II.

2. Proposed Methodology For Palm Extraction

Image prepossessing is conventionally the first and essential step in pattern recognition. In this paper a Method [13] is adopted which uses finger webs as the datum points to develop an approximate Region OF Interest to which changes are made to surmount the limitations of existing method.

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Figure 2: Boundary pixels of palm image

Figure 3 : Distance distribution diagram 2.2 Dynamic ROI Extraction The following steps are performed to ext ract the ROI. 1. The image is then rotated at an angle α to align the straight line joining FW3(x3, y3) & FW1(x1, y1) with the horizontal axis as shown in figure 4.

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Extraction of Dynamic Region of Interest (ROI) for Palmprint using Templates Databases

Figure 8: Boundary of Binary image N plotted using b1 & b2 matrices

Figure 4: Image Q after rotation with finger web 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 the binary image figure 5 and X & Y co-ord inates of all the boundary pixels are stored in different matrices. X co-ordinate values of boundary pixels are stored in b1-matrix and Y co-ordinate values are stored in b2-matrix. Plots between b1-matrix (X-co-ordinate) and boundary pixels and b2-matrix and boundary pixels is shown in figure 6 and figure 7 respectively. The boundary of a binary image obtained by drawing a plot between b1matrix and b2-matix and shown in figure 8.

4. For Width: The maximu m Y-coordinate in the b2- mat rix is calculated using (3) Ym=max (b2)-k (3) where k=15 is chosen empirically for experimental purpose. Then for th is new Ym there will be two X coordinates (say X1 and X2) on the boundary as shown in figure 9 and can be found fro m matrix b 1 wh ich is show in the figure 10a and 10b. Now width of ROI is calculated using (4). 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) against the boundary pixels

(a)

(b) Figure 10: a) Max Y-Coordinate and b) X1 and X2 values

Figure 7 : Plot of Y co-ord inates (b2-matrix) against the boundary pixels

For Height: To calculate the height we require maximu m Y-coordinate (Ymax) and minimu m Ycoordinate(Ymin ).The Ymax can be calculated utilizing (5) by subtracting it fro m P which is the length of the image.

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Extraction of Dynamic Region of Interest (ROI) for Palmprint using Templates Databases 1.

Ymax=P-Ym

(5)

The Ymin can be calcu lated utilizing (6) by find the minimu m Y-coordinate out of all three web points after complementing it with the length of image.

Ymin = min( P-y1,P-y2 ,P-y 3)

demonstrates that fine-tuned size ROI cover diminutively minuscule area and valued informat ion is missed where as dynamic size ROI extracts maximu m size ROI and 99.9% ROIs without background information.

The ROI images we obtained fro m each palm image had maximu m Size of ROI 201* 174 and minimu m Size of ROI 137*163 shown in figure 13.

(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 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.

(a)

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 [12].The database was acquired at Hong Kong Polytechnic University (Ch ina) utilizing camera.

In its current version the database contains, 7752(8-b it) grey-scale images corresponding to 386 subjects. The experiment has been performed on a system of 2.0GHz CPU and 256 MB of RAM. Most of the researchers [13-18] ut ilized the PolyU Palmp rint database [12] and they ext racted finetuned size 128* 128 ROI. Result of the proposed Algorith m are co mpared with fine-tuned size ROI extraction Algorithm[13].The experiment

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Palmp rint Image Size of ROI 137 * 163 (b) Figure 13: a) Palmprint Images and corresponding Maximu m size Dynamic ROI Ext racted (201 * 174) b) Palmp rint Images and corresponding Minimu m size Dynamic ROI Ext racted (137 * 163)

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Extraction of Dynamic Region of Interest (ROI) for Palmprint using Templates Databases VI. Conclusions Palm segmentation is the key step in palmprint recognition system. Seg mentation of palm includes separation of palm which is in between the wrist and fingers of hand images. In this paper, we propose a Dynamic ROI extract ion technique depending upon the size of the image.

The proposed method extracts maximu m possible ROI region without background informat ion when compared to the existing fixed ROI extract ing techniques [13-18] .We found that the efficiency of our proposed approach agrees with the other systems in the state of art and is better for the future feature extract ion and matching.

[9]

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