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  • Why are the classes not frequently used in biometric authentication? What is used for biometric classification?

Full n final prjct Full n final prjct Presentation Transcript

  •  features of fingerprint enhancement binarization thinning minutaie detection
  •  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.
  • Fingerprint recognition is one of the oldest and mostresearched fields of biometrics.Some biological principles (Moenssens 1971) relatedto 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.
  • Diagram of AFMS
  • • 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.
  • 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 isgenerally 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 thecenter.Sweat Gland: The moisture and oils they produce actually allow the fingerprint to be electronically imaged.
  • Arch Left Loop Right Loop Whorl undecided
  •  Reliable minutiae extraction is extremely important Enhancement Binarization Thinning
  •  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.
  •  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
  • For each fingerprint image, the fingerprint areas resulting from segmentation can be divided into: Well-defined region Recoverable region Unrecoverable region
  •  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
  •  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
  • A. NormalizationB. Ridge FrequencyC. Wavelet DecompositionD. Orientation Field Estimation
  •  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
  •  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.
  •  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.
  •  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 )
  • 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 >>aa= a= 1 1 2 3 2 3use semi-colon use commato separate rows to separate columns
  • 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
  • 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)
  • 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
  • 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
  • Built-in MATLAB FunctionsMath 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);
  • 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)
  •  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
  •  Syntax- imread(‘filename’)Ex. F=imread(‘chestxray.jpg’); F=imread(‘d:myimageschestxray.jpg’); F=imread(‘.myimageschestxray.jpg’);
  •  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)
  •  Syntax- Imshow(f,g)Where f=image arrayG=no. of intensity level used to display it.• Imshow(f.[low high])
  • 1. Intensity type imagesa data values whose intensity has been scaled to represent intensities.eg.- scaled uint8,scaled uint162. Binary type imagesLogical arrays of 0s &1sS=logical(a)Where a is numerical array
  •  Uint8 [0 255] Uint16 [0 65535] Double -10e308 to10e308 Int8 [-128 127] Int16 [-32768 32767] Single -10e38 to 10e38
  •  Im2uint8 Mat2gray Im2double im2bw
  •  TIFF JPEG GIF BMP PNG XWD
  •  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)
  • >>%code for vertical flip of image>> f=imread(finger.png.jpg);>> imshow(f)>> fb=f(end:-1:1,:);>> figure,imshow(fb)
  • >> f=imread(finger.png.jpg);>> imshow(f)>> fc=f(57:168,57:168);>> figure,imshow(fc)
  • >> %code for sub sample of image>> f=imread(finger.png.jpg);>> imshow(f)>> fs=f(1:4:end,1:4:end);>> figure,imshow(fs)
  • >>
  • ENHANCEMENT includes 2 steps binarization thinning
  •  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
  • 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]);
  • Fingerprint is thinned becoz after being thinned, it is easier to find minutiae such as bifurcation and simple end points.
  •  Reduces the width of the ridges to one pixel Skeletons , spikes Filling holes, removing small breaks, eliminating bridges between ridges etc.
  • 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
  • K=bwmorph(~J,thin,inf);Imshow(~K);set(gcf,position,[1 1 600 600]);
  • 3 cmds can be used to clear out the window clear all close all clc all