Final_Master's Project Report - Ziad Siddique

8/3/2019 Final_Master's Project Report - Ziad Siddique

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FINGERPRINT IDENTIFICATION FOR CYRIX MEDIAGX BASED EMBEDDED SYSTEM

Ziad S Siddique

B.S., The University of Mississippi, 2006

PROJECT

Submitted in partial satisfaction of

the requirements for the degree of

MASTER OF SCIENCE

in

COMPUTER ENGINEERING

at

CALIFORNIA STATE UNIVERSITY, SACRAMENTO

SPRING2010


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A Project

by

Ziad S Siddique

Approved by:

__________________________________, Committee ChairJing Pang, Ph. D.

__________________________________, Second ReaderPreetham Kumar, Ph. D.

____________________________Date


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Student: Ziad S Siddique

I certify that this student has met the requirements for format contained in the University format

manual, and that this project is suitable for shelving in the Library and credit is to be awarded for

the Project.

__________________________, Graduate Coordinator ________________Suresh Vadhva, Ph. D. Date

Department of Computer Engineering


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Abstract

of


by

Ziad S Siddique

Nowadays portable devices like cell phones, smart phones, PDA, net books and laptops

have become household items. People, for their own convenience love taking computers

or personal data with them at all times. Having these portable devices with us enables

frequent access to mails, lookup someones information in an easily accessible and

searchable form and much more information we need can be obtained instantly. The very

portability of these devices presents a problem: In the case of any of these devices getting

lost or stolen, it will provide other people access to our emails, photos and other personal

information.

Biometric Fingerprint readers offer an ideal solution to this problem. It is much

better than very secure password, and a fingerprint match is required before gaining

access to information in specific devices. In this project, I have researched and

implemented a Fingerprint Identification Process application for a specific embedded

system. In conclusion, this project identified mechanisms and features of Fingerprint

Identification that can be utilized for application and implementation in specific systems.

_______________________, Committee ChairJing Pang, Ph. D.

_______________________Date


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ACKNOWLEDGMENT

First, I would like to extend my gratitude to my advisor Dr. Jing Pang for the invaluable

advice and positive encouragement she provided throughout the course of this project.

She inspired me greatly to work on this project. Special thanks to Dr. Preetham Kumar

for proofreading this dissertation. Besides, I would also like to thank The Department of

Computer Engineering for providing me a good environment and facilities to complete

this project successfully.


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TABLE OF CONTENTS

Page

Acknowledgment .......................................................................................................... v

List of Tables ............................................................................................................. vii

List of Figures ........................................................................................................... viii

Chapter

1. INTRODUCTION .................................................................................................. 1

2. CYRIX MEDIAGX ARCHITECTURAL SYSTEM OVERVIEW ....................... 2

2.1 Cyrix MediaGX Processor Overview ......................................................... 4

2.2 Cyrix MediaGX Cx5510 Processor Overview ........................................... 4

2.3 Compatible Operating System and Compiler ............................................. 5

3. FINGERPRINT IDENTIFICATION PROCESS IMPLEMENTATION ............. 6

3.1 Design Flow ................................................................................................ 7

3.2 BMP File Format ........................................................................................ 8

3.3 Image Normalization ............................................................................... 11

3.4 Edge Detection .......................................................................................... 15

3.5 Image Binarization .................................................................................... 18

3.6 Noise Filter ................................................................................................ 20

3.7 Image Thinning ........................................................................................ 23

3.8 Minutiae Extraction ................................................................................. 28

3.9 Experimental Result .................................................................................. 32

4. LIMITATIONS .................................................................................................... 33

5. CONCLUSIONS................................................................................................... 35

Bibliography ............................................................................................................... 36


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LIST OF TABLES

Page

1. Table 1 BMP (windows) header format (54 bytes) ......................................... 9

2. Table 2 5X5 Laplacian of gaussian convolution kernel ................................. 17

3. Table 3 3X3 Window frame for noise filter ................................................... 20

4. Table 4 Properties of crossing number .......................................................... 29

5. Table 5 Experimental result of minutiae extraction ....................................... 32


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LIST OF FIGURES

Page

1. Figure 1 Cyrix mediagx system architecture ................................................... 2

2. Figure 2 Fingerprint identification design flow ............................................... 7

3. Figure 3 Bitmap image format ......................................................................... 8

4. Figure 4 Data offset starting location equation .............................................. 10

5. Figure 5 Histogram distribution of grayscale image ...................................... 11

6. Figure 6 Weighted arigthmatic mean ............................................................. 12

7. Figure 7 Population variance ......................................................................... 12

8. Figure 8 Image normalization equation ......................................................... 13

9. Figure 9 Normalized bitmap image ............................................................... 14

10. Figure 10 Graphical representation of laplacian method ............................... 15

11. Figure 11 Laplacian equation ......................................................................... 16

12. Figure 12 Laplacian of gaussian equation ..................................................... 16

13. Figure 13 Edge detected bitmap image .......................................................... 17

14. Figure 14 Bi-modal intensity distribution ...................................................... 18

15. Figure 15 Binarized bitmap image ................................................................. 19

16. Figure 16 Noisy white pixel conversion ........................................................ 20

17. Figure 17 Noisy black pixel conversion ........................................................ 20

18. Figure 18 Eight combination to determine white pixel validity .................... 21

19. Figure 19 Eight combination to determine black pixel validity .................... 21

20. Figure 20 Filtered bitmap image .................................................................... 22


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21. Figure 21 3X3 Window frame for hilditch algorithm .................................... 23

22. Figure 22 Eight combinations for 1st

level hilditch algorithm ....................... 24

23. Figure 23 Eight combinations for 2nd

level hilditch algorithm ...................... 24

24. Figure 24 Cases to retain pixel value ............................................................. 25

25. Figure 25 Thinned bitmap image ................................................................... 27

26. Figure 26 3X3 window frame for minutiae extraction .................................. 28

27. Figure 27 Crossing number equation ............................................................. 28

28. Figure 28 Example of crossing number properties ........................................ 29

29. Figure 29 Example of false minutiae structure .............................................. 30

30. Figure 30 Angle shown for minutiae point .................................................... 30

31. Figure 31 Polar coordinates equation ............................................................ 31

32. Figure 32 Thinned bitmap image for minutiae extraction ............................. 31


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Chapter 1

INTRODUCTION

Nowadays portable devices like cell phones, smart phones, PDA, net books and laptops

have become common household items. Having these portable devices with us at all

times enables frequent access to mails, lookup information in an easily accessible and

searchable form, and much important information that we need for daily usage can be

obtained instantly. However, due to the portability of these devices, in the case of losing

or getting any of these devices stolen, it can provide others access to our emails, photos

and many other personal information.

Biometrics offers an ideal solution to this problem. Biometrics takes our unique

physical characteristics and uses them to identify our identification and verification. It is

much better than even very secure passwords, and an identity match is required before

one can gain access to the information in specific devices. This project looked into

biometrics Fingerprint Identification Process implementation for a specific embedded

system.

As a first phase of the project, I researched the architectural overview of Cyrix

MediaGX based embedded system. In addition, I researched and tried installing different

operating systems that would be compatible with the instruction set of the system.

During the second phase of the project, I identified and implemented fingerprint

identification that included sequence of image enhancement and identity point collection

for better verification results.


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Chapter 2

CYRIX MEDIAGX ARCHITECTURAL SYSTEM OVERVIEW

The introducing of the Cyrix MediaGX system in February 1997 established a new class

of low cost, high performance PC architectures [1]. The general principle is that more

processing that occurs in PCs CPU side, the more efficient the overall system

performance. The MediaGx architecture integrates graphics and audio functions, PCI

Interface and memory control unit into processor unit. This feature eliminates potential

system conflicts and end user configuration problems. The MediaGX system consists of

two chipsthe Cyrix MediaGX processor and Cyrix MediaGX Cx5510 companion chip

[2]. The audio and video functions of the two chips operate under the control of the

Virtual System Architecture (VSA) design.

Figure 1: Cyrix mediagx system architecture [2]


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In figure 1, it shows the architectural overview of a Cyrix MediaGX based system. Where

communication is established between CPU and companion chip Cx5510 through PCI

Bus. A 64 bit Data Bus is present to access EDO DRAM memory. In addition,

companion chip Cx5510, connects to the I/O devices through ISA and IDE Bus.


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2.1 Cyrix MediaGX Processor Overview:

MediaGx processor is an x86 compatible processor that directly communicates with PCI

interface and EDO DRAM over a dedicated 64-bit Data Bus. High quality SVGA is

provided by advance graphics accelerator on the MediaGX processor [2]. Graphics are

handled by dedicated pipeline on the CPU itself and display controller is also located at

main processor. Cyrix MediaGX processor core operates at 3.3 or 3.6 volt power supply.

Among other features, it includes [2]

PR (Performance Rating)-133 performance at 133 MHzIntegrated Floating Point Unit

16-KByte Unified L1 Cache

64-bit FPM/EDO DRAM Controller

2.2 Cyrix MediaGX Cx5510 Processor Overview:

Cyrix MediaGx Cx5510 is a new generation integrated, single chip controller for

MediaGX line of x86 compatible. This companion chip houses the audio controller and

uses Virtual System Architecture software to mimic the functionality of industry standard

audio chips. The Cx5510 is a bridge to ISA bus, IDE and I/O ports [2]. It bridges

MediaGX processor over PCI Bus to ISA Bus and performs traditional chipset functions.


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Chapter 3

FINGERPRINT IDENTIFICATION PROCESS IMPLEMENTATION

Fingerprints are the oldest and most widely used biometric form for identification

because of their high acceptability [5]. There have been considerable amount of interests

and rapid advanced research and development in the field of pattern recognition

automation for last three decades. Today, the use of computers in fingerprint

identification is highly desirable in many applications. Building security systems and

work related to law-enforcement are most common examples of usage of fingerprint

identification and recognition process [6]. This project focuses on the software

implementation of fingerprint identification process. The most commonly used Minutiae

Based Fingerprint Identification Process is introduced as part of software development.

Series of image enhancement and Minutiae Extraction steps are included in the process

that can be classified as following phases [5]:

1. Histogram Normalization

2. Edge Detection

3. Image Binarization

4. Noise Filter

5. Image Thinning

6. Minutiae Extraction

A specific image format, BMP file format is taken into consideration for this project

work.


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Input

Bitmap Image (.BMP)

Apply Edge Detection

(Laplacian Algorithm)

Edge Detected

Bitma Ima e .BMP

Apply Noise Filter

Apply Binarization

Binarized

Bitmap Image (.BMP)

ApplyThinning

(Hilditch Algorithm)

Thinned

Bitmap Image (.BMP)

Final

Minutiae Extracted

Data File (.TXT)

ApplyMinutiae ExtractionAlgorithm

Apply Histogram Normalization

Normalized

Bitma Ima e .BMP

Filtered

Bitmap Image (.BMP)

3.1 Design Flow:

Figure 2: Fingerprint identification design flow


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3.2 BMP File Format

Windows bitmap files are stored in a device independent format so that the bitmap can be

displayed in any display device. The term Device Independent means bitmap specifies

pixel color in a form which is independent of the method used by display to represent

color [7]. By default a windows bitmap file extension is .BMP. Each individual Image

file formal is structured uniquely. In the process of image manipulation, it is very

important to understand a particular image file format. Image file format consists of a

header and data offset information. BMP image file format is structured with bitmap file

header, bitmap information header, a color table, and array of bytes representing the

bitmap bits also known as data offset [8].

Figure 3: Bitmap image format [8]


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offset Size(bytes) Description

0 2 Signature

2 4 size of BMP file

6 2 Reserved

8 2 Reserved

10 4 offset to start of image data in bytes

14 4 size of BITMAPINFOHEADER structure, must be 40

18 4 image width in pixels

22 4 image height in pixels

26 2 number of planes in the image

28 2 number of bits per pixel

30 4 compression type

34 4 size of image data in bytes

38 4 horizontal resolution in pixels per meter

42 4 vertical resolution in pixels per meter

46 4 number of colors in image

50 4 number of important colors

Table 1: BMP (windows) header format (54 bytes) [9]


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A part of function used to retrieve FileSize, ImageWidth, ImageHeight,NumberOfColors:

fseek(inputFile, offset, SEEK_SET);

for(i=1; i


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3.3 Image Normalization:

Image enhancement is one of the very important procedures in fingerprint Identification

Process. Image normalization is well known in fingerprint enhancement process. It is

necessary to apply normalization on overexposed (too bright) or underexposed (too light)

gray scale input image [10]. The aim of this process is to standardize the intensity of an

image by adjusting the grey level value so that it lies within desired range of values [11].

Normalization factor is calculated according to the Mean and Variance of an image.

Different grayscale image input images have different Mean and Variance depending on

the lighting conditions. In order to change the appearance of the image several

calculations are performed to approximate desired Mean and Variance [10]. It is required

to construct a histogram with pixel intensity values for obtaining desired mean and

variance for one given input image. The histogram distribution shown in figure 5 is

obtained by collecting pixels intensity for a given gray scale input image.

Figure 5: Histogram distribution of grayscale image [12]


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In an image-processing context, histogram normally refers to the graphical distribution of

pixel intensity values. There are 256 different possible intensities for 8 bits per pixel

grayscale image. The histogram will graphically display those 256 numbers with the

distribution of pixels amongst those grayscale values. The histogram algorithm operates

by first reading the grayscale value with pixel intensity 0 to 255 at the first entry [12].

The array of pixel intensity counts total number of pixels that has the same value. Then

the frequency is calculated diving the counter values for each pixel by the total number of

pixels.

Mean and Variance value calculation can be achieved using the mean and variance

arithmatic equations on the collected histogram data.

n

i i

ni ii

W

W

1

1(3.3.1)

Figure 6: Weighted arithmatic mean [13]

As shown in the figure 6, representing it into code:

Mean = 0;

for(i=1; i


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As shown in the figure 7, is the Variance and representing it into code:

Sigma = 0;

for(i=0; i


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Normalization does not change the ridge structure of an image. It is performed to

standardise the dynamic level of variation in gray level values. The resulting image

shown in Figure 9 is obtained by applying histogram normalization on a grayscale image.

Input: Grayscale image Output: Normalized image

Figure 9: Normalized bitmap image


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3.4 Edge Detection:

Edges characterizes image boundaries and it is very important in image processing.

Edges are the areas in an image with high intensity contrasts, change of intensity in pixel

value from one pixel to neighboring pixels. Image Edge Detection preserves the

structural properties of an image by reducing large amount of variable data and filtering

out useless information [15]. The result of successful implementation of edge detection

substantially simplifies the task of interpreting information from original image.

However, it is not always possible to obtain ideal edge detection for moderately complex

images. There are many ways to perform Edge Detection. Majority of the different

methods are categorized into group in two ways, either gradient and Laplacian [15].

The Gradient Method detects the edges by looking for the maximum and minimum value

from the first derivative of the image. Laplacian Method searches for the zero crossing on

the second derivative of images to find the edges.

Actual Data Point

First Order Derivative Second Order Derivative

Figure 10: Graphical representation of laplacian method [15]


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Laplacian of Gaussian filter is also known as convolution filter, and is implemented to

detect edges for a given image. This filter applies Gaussian blur in first level, then

Laplacian filter and finally checks for zero crossings [16]. Highlighted edges are result of

this filter. The Laplacian of the Gaussian filter operator takes single grayscale image as

input and generates an output close to binary image [16].

Laplacian L(x, y) operator of an image with pixel intensity of I(x, y) is shown below:

2

2

2

2

,y

I

x

IyxL (3.4.1)

Figure 11: Laplacian equation [16]

Laplacian of Gaussian can be pre-calculated in advance and only one convolution filter

will be applied on the image during run time. 2-D Laplacian of Gaussian function

centered on zero with standard deviation has the form:

2

22

2

22

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21

1,

yx

eyx

yxLoG (3.4.2)

Figure 12: Laplacian of gaussian equation [16]

Where LoG is a 2-D Laplacian of Gaussian function with coordinates x, y and is the

standard deviation.


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The 2-D Laplacian of Gaussian (LoG) can be approximated by a 5X5 convolution kernel,

such as:

0 0 1 0 0

0 1 2 1 0

1 2 -16 2 1

0 1 2 1 0

0 0 1 0 0

Table 2: 5X5 Laplacian of gaussian convolution kernel [17]

The code base implementation of edge detection using Laplacian of Gaussian

convolution kernel as shown in table 3:

for(row=0; row


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3.5 Image Binarization:

Fingerprint Binarization is a very important part of Fingerprint Identification System.

Minutiae Extraction algorithm is applied on a binary image with two level of intersection:

the black pixel represents valleys and the white pixel represents ridges [11]. Binarization

is the process of converting a gray image to binary image with only black and white pixel

values, which improves the contrast between ridges and valleys [11]. Threshold Value is

applied on the gray image for the binary image conversion. If the pixel intensity value is

higher than the Threshold, it will be set to 255 (White). Otherwise, it will be set to 0

(Black) [18]. In the output binary image, black pixels correspond to background and

white pixels correspond to foreground. It is possible to segment an image by checking the

intensity histogram of the image. The intensity of pixels within foreground objects is

different from the intensity of pixels within background. In this case, distinct peaks are

expected in the histogram corresponding to foreground objects such that a threshold can

be chosen to isolate the foreground and background objects accordingly.

Figure 14: Bi-modal intensity distribution [19]


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3.6 Noise Filter:

Noise reduction is a process of filtering a specific pixel data offset depending on

surrounding pixel value. In this project, the noise filter is utilized right after converting a

gray image into binary image. A 3X3 matrix positioning is used to filter an image.

Top Left Pixel Data Top Pixel Data Top Right Pixel Data

Left Pixel Data Actual Pixel Data Right Pixel Data

Bottom Left Pixel Data Bottom Pixel Data Bottom Right Pixel Data

Table 3: 3X3 Window frame for noise filter

This positioning process determines the validity of actual pixel data. In case of any pixel

with value 255 (white) surrounded by pixels with values 0 (black), this pixel is

considered as noise or invalid data. For the same reason pixels with 0 value surrounded

by pixels with values 255.

Figure 16: Noisy white pixel conversion

Figure 17: Noisy black pixel conversion

As shown in figure 16, a single white pixel was removed as noise and replaced with a

black pixel. In figure 17, a single black pixel was removed as noise and replaced with a

white pixel. Eight combinations have been taken into consideration to identify a valid


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white or black pixel value. In 3X3 matrix positioning, the middle location is the current

processed pixel data. A valid pixel value would retain original data if its one or more

surrounding location pixels have the same value as the actual pixel data.

Figure 18: Eight combinations to determine white pixel validity

Figure 19: Eight combinations to determine black pixel validity


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3.7 Image Thinning:

Image thinning is final image enhancement process implemented prior to Minutiae

Extraction. Thinning, a morphological process applied on a binary image to remove

selected foreground pixels [20]. Thinning algorithm forms skeletonization of a binary

image while preserving the connectivity of the ridge structure [11]. In this mode it is

commonly used to erode away foreground pixels until the images edges are one pixel

wide. Thinning is normally only applied on a binary image and the output is also a binary

image. Different algorithm uses different approaches to solve the problem of thinning.

Thinning algorithms are broadly divided into two groups: iterative and non-iterative [21].

Iterative algorithm is also subdivided into two parts: sequential and parallel. In sequential

iterative algorithm, surrounding pixels help making the decision for single pixel data. On

the other hand, in parallel iterative algorithm pixels are independently judged for

decision-making [22, 23]. This thinning phase is implemented using Hilditch algorithm.

The proposed algorithm belongs to sequential group. The 3X3 window frame is

considered to implement Hilditch algorithm as follows:

Figure 21: 3X3 Window frame for hilditch algorithm [24]

Where, pixel P0 is actual pixel with set of 8 Neighborhoods P1-8.


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A counter is introduced as part of this algorithm. If the actual foreground pixel is white,

the verified eight combinations are as follows:

255 X X X 255 X X X 255 X X X

X P0 X X P0 X X P0 X X P0 255

X X X X X X X X X X X X

X X X X X X X X X X X X

X P0 X X P0 X X P0 X 255 P0 X

X X 255 X 255 X 255 X X X X X

Figure 22: Eight combinations for 1st

level hilditch algorithm [25]

In the figure 22, eight combinations with actual pixel P0 in the center and X represents

dont care for the individual cases. If it satisfies any of the combination then the counter

is incremented by one for each case. If the counter value falls in between 2 and 6 then the

next combinations verified are as follows:

X X 0 X X X X X X X X X

X P0 255 X P0 0 X P0 X X P0 X

X X X X X 255 X 255 0 255 0 X

X X X 255 X X 0 255 X X 0 255

255 P0 X 0 P0 X X P0 X X P0 X

0 X X X X X X X X X X X

Figure 23: Eight combinations for 2nd level hilditch algorithm [25]

Where clockwise surroundings 255, 0 and X represents white pixel, black pixel and dont

care with centered actual pixel P0.


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The counter is set to zero and incremented by one if any of the cases are satisfied as

shown in figure 23. The last stage, which will determine if the actual pixel value is to be

converted from foreground (white) to background (black) pixel is as follows:

P2

P4 P8 P4

P6 P6

(a) (b)

P2 P2

P8 P8 P4

P6

(c) (d)

Figure 24: Cases to retain pixel value [25]

As shown in figure 24, if the counter value is one from the previous stage and the

multiple of pixel values located at numbered locations of the four stages are zeros then

the actual foreground pixel is converted to a background pixel. To be more specific if,

P2* P4* P6=0 from stage (a) and P4* P6* P8 =0 from stage (b) or P2* P6* P8=0 from

stage (c) and P2* P4* P8 =0 from stage (d) then foreground pixel white (255) is changed

to background pixel black (0) [25]. The code implementation of this algorithm is as

follows:

// foreground actual pixel

if (actual == 255)

{

count = 0;

//a) checking pixel 1, has 2 to 6 (inclusive) neighbors

if (right == 255) { count++;}if (bottomRight == 255) { count++;}

if (bottom == 255) { count++;}

if (bottomLeft == 255) { count++;}

if (left == 255) { count++;}

if (topLeft == 255) { count++;}

if (top == 255) { count++;}

if (topRight == 255) { count++;}


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if ((count >= 2) && (count


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The images obtained in the process of three level thinning are as follows:

1st level Thinning:

Input: Binarized image Output: 1st

level Thinned image

2nd level Thinning:

Input: 1st

level Thinned image Output: 2nd

level Thinned image

Final Thinning:

Input: 2nd

level Thinned image Output: Final Thinned image

Figure 25: Thinned bitmap image


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Using the properties of Crossing Number (CN) as shown in the table 1, ridge pixel can be

classified as ridge ending, bifurcation or no minutiae point. A ridge pixel with CN value

of one corresponds to ridge ending and CN value three corresponds to bifurcation.

CN Property

1 Ridge Ending

2 No Minutiae Point

3 Bifurcation

Table 4: Properties of crossing number [11]

(a) CN=1 (b) CN=2 (c) CN=3

Ridge Ending No Minutiae Point Bifurcation

Figure 28: Example of crossing number properties [29]

All the preprocessing stages including normalization, image enhancement, filtering, and

thinning do not guarantee 100% heal fingerprint identification. False minutiae such as

breaking ridges, spurious ridges and holes, can be introduced due to blurry noisy images

and image manipulation through thinning process [30]. So, after minutiae are extracted it

is necessary to implement postprocessing algorithm to eliminate false minutiae.


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(a) Spur (b) Hole (c) Triangle (d) Spike

Figure 29: Example of false minutiae structure [11]

As shown in the figure 29, spur will create false ridge ending, hole and triangle both will

create false bifurcation and spike will create both false ridge ending and bifurcation.

Minutiae point can be validated using postprocessing algorithm proposed by Tico and

Kuosmanen [31]. Similar to other techniques, this algorithm operates on thinned

(skeleton) image. This postprocessing is not implemented as a part of this project. To

determine the actual position and location apart from x and y coordinate of minutiae

point, it requires to finding angles of minutiae with respect to origin (0,0) pixel.

(a) Ridge Ending (b) Bifurcation

Figure 30: Angle shown for minutiae point [32]

Where X0 and Y0 are coordinates for Minutiae point and is the angle.


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As shown in the figure 30, it is possible to find an angle for a known x and y coordinate

of minutiae point. The equations to convert a Cartesian coordinates to Polar Coordinates

to determine the angle are as follows:

FunctionTangentx

ya

TheoremPythagorasyxr

tan

22

(3.8.2)

Figure 31: Polar coordinates equation [33]

Where (x, y) are Cartesian Coordinates and (r, ) are Polar Coordinates.

As mentioned earlier Minutiae Extraction is applied on a thinned image. The image used

for minutiae extraction is shown below:

Figure 32: Thinned bitmap image for minutiae extraction


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3.9 Experimental Result:

Part of the Minutiae Extraction experimental result is shown in table 5. First column in

the table 2 represents minutiae points whether it is ridge ending (1) or bifurcation (3).

The angle is calculated using the Polar coordinates equation as shown in figure 31,

with respect to (0,0) origin. The X and Y are respectively Cartesian coordinates for a

minutiae point.

Crossing Number Angle X Y

3 270 113 86

1 225 114 89

3 67 135 90

3 135 114 92

1 225 116 92

3 292 115 93

3 45 127 94

1 315 110 99

3 67 124 102

3 270 125 102

1 90 115 103

3 292 125 103

3 292 110 1041 22 108 105

3 292 110 105

3 67 69 106

3 67 108 106

1 202 128 121

3 67 128 122

3 67 54 124

3 45 54 125

3 202 55 125

3 67 130 125

1 292 123 126

Table 5: Experimental result of minutiae extraction


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Chapter 4

LIMITATIONS

Cyrix MediaGX based Embedded System and Fingerprint Identification application have

some limitations.

In terms of hardware limitation, this system has only 64 Megabytes of Dynamic

Random Access Memory. Any image manipulation requires more than 64 Megabytes of

dynamic memory access will end up in memory out of allocation error. As a result,

implementation requires complex calculation to determine if the dynamic memory access

falls into the range of the limited memory. Both the system and the hard drive require

twelve volts, five volts and ground source connection. A parallel power supply splitting

from same source will not have enough voltage to power up both devices. To resolve this

issue, two separate sources of power supply are provided to power up the system board

and hard drive.

In terms of software limitation, the application will only accept images with

image height and width less than or equal to 128. This limitation is directly related to the

hardware limitation. As this system can only handle up to 64 megabytes of dynamic

memory, any image greater than the size mentioned above will violate the rule. The

different image enhancement and manipulation operations might result in losing

significant segment of fingerprint data. In addition, the false minutiae points were not

totally filtered at the last phase of minutiae extraction. These constraints falls into

consideration while collecting the minutiae points for a given image. As a result, the


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fingerprint matching will provide 70-80 percent accurate result for a given collected

Minutiae point.


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Chapter 5

CONCLUSIONS

The primary goal of this project is to identify, implement and illustrate an application for

a specific embedded system that can be utilized in terms of the security of system

information. Today, for securing information, many portable devices are equipped with

latest biometrics identification tools, and Biometric enabled intelligence tools have

quickly become accepted as immediate solution of identity problems. Examples include,

but are not limited to face recognition, fingerprint recognition, DNA, hand and palm

geometry and iris recognitions. If any of these identification processes fail to verify an

individual, then it will be quite impossible to access any personal or private information

from the system. There are still lots of provisions in improving biometrics to achieve

accuracy in terms of indentifying exact individual. Improving identification algorithm,

having clearer input data and improving many other constraints will provide high

successful ratio for achieving desired results.

In this project, I have successfully implemented fingerprint identification process

to secure the personal information of a system. In addition, I have presented algorithms to

improve quality of scanned image so that accurate data can be collected for identification.

Next, a matching technique can be implemented utilizing valuable information provided

from this project and enhance the feature of algorithm implemented for more accurate

result.


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Final_Master's Project Report - Ziad Siddique