2.  features of fingerprint
 enhancement
binarization
thinning
 minutaie detection

3.  A fingerprint is the pattern of ridges and valleys on the
surface of a fingerprint. Minutiae are local discontinuities
in the fingerprint pattern. Ex-ridge ending and ridge
bifurcation.
 Ridges and valleys in a local neighborhood form a
sinusoidal-shaped plane wave, which has a well-defined
frequency and orientation.
 In many cases, fingerprints are with numerous
discontinuous ridges (dry, wet, damped, scars and
smudges).
 the main difficulty for feature extraction is that fingerprint
quality is often too low, thus noise and contrast deficiency
can produce false minutiae or hide valid ones.
 Ex- when the person has cuts or scars in his/her fingers.

4. Fingerprint recognition is one of the oldest and most
researched fields of biometrics.
Some biological principles (Moenssens 1971)
related
to fingerprint recognition are as follows:
• Individual epidermal ridges and furrows have
different characteristics for different fingerprints.
This forms the foundation of fingerprint
recognition
• The configuration types are individually variable;
but they vary within limits that allow for a
systematic classification.
Herein lies the basis for fingerprint
classification.
• The configuration and minute details of furrows
are permanent and unchanging.

6. Diagram of AFMS

7. • The human fingerprint is comprised of various types of ridge patterns.
• Traditionally classified according to the decades-old Henry system: left
loop, right loop, arch, whorl, and tented arch.
• Loops make up nearly 2/3 of all fingerprints, whorls are nearly 1/3, and
perhaps 5-10% are arches.
• These classifications are relevant in many large-scale forensic applications,
but are rarely used in biometric authentication.

8. Bifurcation: It is the intersection of two or more line-types which converge or diverge.
Arch: They are found in most patterns, fingerprints made up primarily of them are called “Arch Prints”.
Loop: A recursive line-type that enters and leaves from the same side of the fingerprint.
Island: A line-type that stands alone.( i.e. does not touch another line-type)
Ellipse: A circular or oval shaped line-type which is generally found in the center of the fingerprint, it is
generally found in the Whorl print pattern.
Tented Arch: It quickly rises and falls at a steep angle. They are associated with “Tented Arch Prints”.
Spiral: They spiral out from the center and are generally associated with “Whorl Prints”.
Rod: It generally forms a straight line. It has little or no recurve feature. They are gennerally found in the
center.
Sweat Gland: The moisture and oils they produce actually allow the fingerprint to be electronically imaged.

9. Arch Left Loop Right Loop Whorl undecided

10.  Reliable minutiae extraction is extremely
important
 Enhancement
 Binarization
 Thinning

11.  many algorithms and techniques proposed and applied to
fingerprint image enhancement: using Fourier transform,
Gabor filters, Wavelet transform and minutiae filtering,
applied to binary or gray-scale images.
 The main of an enhancement algorithm is to improve the
clarity of ridge structures of fingerprint images in recoverable
regions and to remove the unrecoverable regions.
 the pattern is related to the ridge direction. the enhancement
can help visualizing the ridges.
 In this work, a directional wavelet transform is applied to
decompose the image into its orientation representation.
 Directional filtering is applied to each direction before image
reconstruction.

12.  Performance depends on quality of images
 Ideal fingerprint
 Degradation types – ridges are not continuous, parallel
ridges are not well separated, cuts/creases/bruises
 Leads to problems in minutiae extraction

13. For each fingerprint image, the fingerprint areas
resulting from segmentation can be divided into:
 Well-defined region
 Recoverable region
 Unrecoverable region

14.  Goal – to improve the clarity of the ridge structure
in the recoverable regions and mark
unrecoverable regions as too noisy for further
processing
 Input – a gray-scale image
 Output – a gray-scale or binary image depending
on the algorithm
 Effective initial steps - Contrast stretching,
Histogram manipulation, Normalization, Wiener
Filtering

16.  Most widely cited fingerprint enhancement is by use
of wavelet transform and Gabor filtering.
 It uses wavelet transform for demising and increases
the contrast between the ridge and background
(valley) by using a map function to the wavelet
coefficient set, and thereafter, the Gabor filter method
can further enhance the ridge using the orientation
and frequency information.
 In modified second derivative Gaussian filter ,we
decompose the fingerprint image before directional
filtering.
 The second derivative of Gaussian filter is applied
directly to each sub-image.
 We reconstruct the fingerprint image by using the

17. A. Normalization
B. Ridge Frequency
C. Wavelet Decomposition
D. Orientation Field Estimation

18.  The processing of fingerprint normalization
can reduce the variance in gray-level values
along ridges and valleys .
 It adjusts the gray-level values to the
predefined constant mean and variance.
 Normalization can remove the influences of
sensor noise and gray-level deformation

19.  In a gray scale image, repeated ridges and valley
appearance of fingerprint patterns can be viewed as a
sinusoidal shape with some particular frequency..
 The inverse of the average distance between the
numbers of peaks encountered is the local frequency
of that block.
 In our case, the ridge frequency of 0.10-0.12 was
measured.

20.  Different base function convolution with the
image can have different effect in the
resolution.
 we can decompose the image into sub-images
at any level.
 However, too low resolution is not suitable
because an excessive down sampling of the
signal can vanish the orientation characteristic
of the ridge structure.
 We have used only one decomposition level.

24.  The fundamental unit of data in MATLAB
 Scalars are also treated as arrays by MATLAB (1
row and 1 column).
 Row & column indices of an array start from 1.
 Arrays can be classified as vectors and matrices.
 Vector: Array with one dimension
 Matrix: Array with more than one dimension.
 Total number of elements in an array=
number of rows(n) * number of columns(m)
 Size of an array –(n x m )

25. Initializing Variables in Assignment
Statements
column vectors row vectors
1 
 
a = 2 a = {1 2 3}
3 
 
>>a=[1;2;3] >>a=[1,2,3]
>>a >>a
a= a=
1 1 2 3
2
3
use semi-colon use comma
to separate rows to separate columns

26. Initializing with Shortcut Expressions
first: increment: last
• Colon operator:
a shortcut notation used to initialize arrays with thousands of
elements
>> x = 1 : 2 : 10;
>> angles = (0.01 : 0.01 : 1) * pi;
• Transpose operator: (′)
swaps the rows and columns of an array 1 1
2 2
>> g = [1:4]; h=
3 3
>> h = [ g′ g′ ]; 4 4

27. The end function
• The end function: When used in an array subscript, it
returns the highest value taken on by that subscript.
arr3 = [1 2 3 4 5 6 7 8];
arr3(5:end) is the array [5 6 7 8]
arr4 = [1 2 3 4; 5 6 7 8; 9 10 11 12];
arr4(2:end, 2:end)

28. MATLAB supports six relational operators.
 Less Than <
 Less Than or Equal <=
 Greater Than >
 Greater Than or Equal >=
 Equal To ==
 Not Equal To ~=
MATLAB supports three logical operators.
 not ~ % highest precedence
 and & % equal precedence with or
 or | % equal precedence with and

29. Built-in MATLAB Functions
• result = function( input )
– abs, sign
– log, log10
– exp
– sqrt;
– sin, cos, tan
– asin, acos, atan
– max, min
– round, floor, ceil, fix
– mod, rem
• help elfun → help for elementary math functions

30. Built-in MATLAB Functions
Math representation Matlab interpretation
z = yx >>z=y^x;
y = ex >>y=exp(x);
y = ln(x) >>y=log(x);
y = log(x) >>y=log10(x)
y = sin(x) y = sin −1 (x) >>y=sin(x); >>y=asin(x);
y = cos(x) y = cos −1 (x) >>y=cos(x); >>y=acos(x);
y = tan(x) y = tan −1 (x) >>y=tan(x); >>y=atan(x);

31. x = 0:pi/100:2*pi;
y = sin(x);
plot(x,y)
xlabel('x = 0:2pi')
ylabel('Sine of x')
title('Plot of the Sine
Function')

32.  A digital image is a representation of a two-dimensional
image as a finite set of digital values, called picture elements
or pixels
 Pixel values typically represent gray levels, colours, heights,
opacities etc
 Remember digitization implies that a digital image is an
approximation of a real scene
1 pixel

33.  Syntax-
imread(‘filename’)
Ex.
 F=imread(‘chestxray.jpg’);
 F=imread(‘d:myimageschestxray.jpg’);
 F=imread(‘.myimageschestxray.jpg’);

34.  Syntax
imwrite(f,’filename’)
Ex-
 Imwrite(f,’patient10_run’,tif);
or
 Imwrite(f, ’patient10_run.tif’);
For jpeg file
 Imwrite(f,’filename.jpg’,’quality’,q)

35.  Syntax-
Imshow(f,g)
Where f=image array
G=no. of intensity level used to display it.
• Imshow(f.[low high])

36. 1. Intensity type images
a data values whose intensity has been scaled to represent intensities.eg.-
scaled uint8,scaled uint16
2. Binary type images
Logical arrays of 0s &1s
S=logical(a)
Where a is numerical array

37.  Uint8 [0 255]
 Uint16 [0 65535]
 Double -10e308 to10e308
 Int8 [-128 127]
 Int16 [-32768 32767]
 Single -10e38 to 10e38

38.  Im2uint8
 Mat2gray
 Im2double
 im2bw

39.  TIFF
 JPEG
 GIF
 BMP
 PNG
 XWD

40.  Common image formats include:
 1 sample per point (B&W or Grayscale)
 3 samples per point (Red, Green, and Blue)
 4 samples per point (Red, Green, Blue, and “Alpha”,
a.k.a. Opacity)

41. >>%code for vertical flip of image
>> f=imread('finger.png.jpg');
>> imshow(f)
>> fb=f(end:-1:1,:);
>> figure,imshow(fb)

43. >> f=imread('finger.png.jpg');
>> imshow(f)
>> fc=f(57:168,57:168);
>> figure,imshow(fc)

45. >> %code for sub sample of image
>> f=imread('finger.png.jpg');
>> imshow(f)
>> fs=f(1:4:end,1:4:end);
>> figure,imshow(fs)

46. >>

47. ENHANCEMENT includes 2 steps
 binarization
 thinning

48.  Based on peak detection in the gray-scale profiles along sections
orthogonal to the ridge orientation
 A 16x16 oriented window is centered around each pixel [x,y]
 The gray-scale profile is obtained by projection of the pixel
intensities onto the central section

49. I= imread(‘image.bmp’)
imshow(I)
set(gcf,'position',[1 1 600 600]);
J=I(:,:,1)>160;
Imshow(J);
set(gcf,'position',[1 1 600 600]);

52. Fingerprint is thinned becoz after being thinned,
it is easier to find minutiae such as bifurcation
and simple end points.

53.  Reduces the width of the ridges to one pixel
 Skeletons , spikes
 Filling holes, removing small breaks,
eliminating bridges between ridges etc.

54. Bwmorph: applies morphological operation on
binary image.
Syntax: BW2 = bwmorph(BW,operation,n)
applies the operation n times. n can be Inf, in
which case the operation is repeated until the
image no longer changes

55. K=bwmorph(~J,'thin','inf');
Imshow(~K);
set(gcf,'position',[1 1 600 600]);

58. 3 cmds can be used to clear out the window
 clear all
 close all
 clc all