CFingerPrint C# (Biometric SDK)

/*
Biometric SDK
Version 1.3

This file contains functions that manipulate , extract features and match
fingerprint images.

Copyright (C) 2005 Scott Johnston
Email : moleisking@googlemail.com

This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; either version 2
of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
*/

using System;
using System.Drawing;
using System.Drawing.Drawing2D;

namespace BiometricsSDK.FingerPrint
{
///
/// Summary description for CFinger.
///

public class CFingerPrint
{
//used for image
//for digital persona with kit
//public int FP_IMAGE_WIDTH = 500;
//public int FP_IMAGE_HEIGHT = 500;
//for verifinger with kit
public int FP_IMAGE_WIDTH = 323;
public int FP_IMAGE_HEIGHT = 352;
//Used by template
//be carefull the size of the array must always be 1 larger than a number divisable by 4
public int FP_TEMPLATE_MAX_SIZE = 601;
//used for matching
//the max distance between to points when comparing two points to count as a match
public int FP_MATCH_POINT_DISTANCE_MOVEMENT = 10;
//the max rotation to use when comparint two points to count as a match
public int FP_MATCH_POINT_ROTATION_MOVEMENT = 10;//10;
//a percentage
public int FP_MATCH_THRESHOLD = 55;
//finger print classifications
//Wirbel class
public const int FP_CLASS_WHORL = 1 ;
//lasso class
public const int FP_CLASS_LEFT_LOOP = 2;
public const int FP_CLASS_RIGHT_LOOP = 3;
public const int FP_CLASS_ARCH = 4;
public const int FP_CLASS_ARCH_TENTED = 5;
//fingerprint template values
private const int FP_TEMPLATE_SIZE = 0;
private const int FP_TEMPLATE_ORIGIN_X = 1;
private const int FP_TEMPLATE_ORIGIN_Y = 2;
private const int FP_TEMPLATE_FEATURE_SIZE = 6;
private const int FP_TEMPLATE_SEARCH_RADIUS = 1;
//fingerprint origin values
//private int FP_ORIGIN_SEARCH_RADIUS = 5;
//holds skeletinized image
public byte [,] P = new byte[500,500];

public CFingerPrint()
{
}

public CFingerPrint(int width , int height )
{
FP_IMAGE_WIDTH = width;
FP_IMAGE_HEIGHT = height;
P = new byte[width,height];
}

public CFingerPrint(int width , int height ,int MatchPointDistanceMovement , int MatchPointRotationMovment , int MatchThreshold )
{

FP_IMAGE_WIDTH = width;
FP_IMAGE_HEIGHT = height;
FP_MATCH_POINT_DISTANCE_MOVEMENT = MatchPointDistanceMovement;
FP_MATCH_POINT_ROTATION_MOVEMENT = MatchPointRotationMovment;
FP_MATCH_THRESHOLD = MatchThreshold;
}

public void setFingerPrintImage(Bitmap m_image)
{
try
{
for (int i = 0; i <= FP_IMAGE_WIDTH - 1; i++) { for (int j = 0; j <= FP_IMAGE_HEIGHT - 1; j++) { if ((m_image.GetPixel(i, j).B <= 127) && (m_image.GetPixel(i, j).R <= 127) && (m_image.GetPixel(i, j).G <= 127)) { P[i, j] = 1; } else { P[i, j] = 0; } } } //set edges to 0 for (int i = 0; i <= FP_IMAGE_WIDTH - 1; i++) { P[i, 0] = 0; P[i, FP_IMAGE_HEIGHT - 1] = 0; } for (int j = 0; j <= FP_IMAGE_HEIGHT - 1; j++) { P[0, j] = 0; P[FP_IMAGE_WIDTH - 1, j] = 0; } } catch (Exception ex) { throw ex; } } public Bitmap getFingerPrintImage() { Bitmap imageBuffer = new Bitmap(FP_IMAGE_WIDTH,FP_IMAGE_HEIGHT ); for (int i = 0; i<= FP_IMAGE_WIDTH - 1;i++) { for (int j = 0;j<= FP_IMAGE_HEIGHT - 1;j++) { if (P[i, j] == 1) { imageBuffer.SetPixel(i, j, Color.Blue); } else { imageBuffer.SetPixel(i, j, Color.White); } } } return imageBuffer; } public Bitmap getFingerPrintImageDetail() { //set finger print image Bitmap imageBuffer = new Bitmap(FP_IMAGE_WIDTH,FP_IMAGE_HEIGHT ); for (int i = 0; i<= FP_IMAGE_WIDTH - 1;i++) { for (int j = 0;j<= FP_IMAGE_HEIGHT - 1;j++) { if (P[i,j] == 1) imageBuffer.SetPixel(i,j,Color.Blue); else imageBuffer.SetPixel(i,j,Color.White ); } } //get features double[] m_arr = this.getFingerPrintTemplate(); // int linelength = 5; //draw points Pen penBlue = new Pen(Color.Blue, 1); Pen penRed = new Pen(Color.Red, 1); Pen penGreen = new Pen(Color.Green, 1); Pen penGray = new Pen(Color.Gray, 1); //Graphics gf = Graphics.FromImage(Image.FromHbitmap(m_ImageBuffer.GetHbitmap() )); Graphics gf = Graphics.FromImage(imageBuffer); gf.CompositingMode = CompositingMode.SourceOver; for(int i=7 ; i<=m_arr[0]-1;i=i+6) { if(m_arr[i+4]>1)
{
gf.DrawRectangle(penRed,(int)m_arr[i]+(int)m_arr[1]-3,(int)m_arr[i+1]+(int)m_arr[2]-2,5,5);
}
else if(m_arr[i+4]==1)
{
gf.DrawEllipse(penGreen,(int)m_arr[i]+(int)m_arr[1]-3,(int)m_arr[i+1]+(int)m_arr[2]-2,5,5);
}


}//end for
//draws the origin
gf.DrawLine(penGray,(int)m_arr[1]-5,(int)m_arr[2],(int)m_arr[1]+5,(int)m_arr[2]);
gf.DrawLine(penGray,(int)m_arr[1],(int)m_arr[2]-5,(int)m_arr[1],(int)m_arr[2]+5);
return imageBuffer;
}//end getFingerPrintImageDetail()

public void ThinningHilditch()
{
int change = 1;
bool mbool = true;
while (change != 0)
{
change = 0;
for(int i = 2; i <= FP_IMAGE_WIDTH - 2;i++) { for(int j = 2; j <= FP_IMAGE_HEIGHT - 2;j++) { if (P[i,j] == 1) { short c = 0; //count surrounding 1 //a) Make sure pixel 1, has 2 to 6 (inclusive) neighbors if (P[i,j+1] == 1) { c++;} if (P[i+1,j+1] == 1) { c++;} if (P[i+1,j] == 1) { c++;} if (P[i+1,j-1] == 1) { c++;} if (P[i,j-1] == 1) { c++;} if (P[i-1,j-1] == 1) { c++;} if (P[i-1,j] == 1) { c++;} if (P[i-1,j+1] == 1) { c++;} if ((c >= 2) && (c <= 6 )) { c = 0; //b) starting from 2, go clockwise until 9, and count the //' number of 0 to 1 transitions. This should be equal to 1. if ((P[i-1,j+1] == 0) && (P[i,j+1] == 1)) { c++;} if ((P[i,j+1] == 0) && (P[i+1,j+1] == 1)) { c++;} if ((P[i+1,j+1] == 0) && (P[i+1,j] == 1)) { c++;} if ((P[i+1,j] == 0) && (P[i+1,j-1] == 1)) { c++;} if ((P[i + 1,j-1] == 0) && (P[i,j-1] == 1)) { c++;} if ((P[i,j-1] == 0) && (P[i-1,j-1] == 1)) { c++;} if ((P[i-1,j-1] == 0) && (P[i-1,j] == 1)) { c++;} if ((P[i - 1,j] == 0) && (P[i-1,j+1] == 1)) { c++;} if (c == 1 ) { c = 0; if (mbool == true) { //c) 2*4*6=0 (ie either 2,4 ,or 6 is off) if ((P[i,j+1] * P[i+1,j] * P[i+1,j-1]) == 0 ) { //d) 4*6*8=0 if ((P[i+1,j] * P[i+1,j-1] * P[i-1,j]) == 0 ) { P[i,j] = 0; change++; } } mbool = false; } else { //c) 2*6*8=0 if ((P[i,j+1] * P[i+1,j-1] * P[i-1,j]) == 0) { //d) 2*4*8=0 if ((P[i,j+1] * P[i+1,j] * P[i-1,j]) == 0) { P[i,j] = 0; change++; } } mbool = true; } } } } } } }//End While }//end ThinningHilditchAlgorithim public void ThinningHitAndMiss() { /* * basicly you take all patterns * 111 X1X * X1X or x11 so on * 000 xxX * if these conditions are true then set the middle 1 to 0 */ int c = 1; while (c != 0) { c = 0; for(int i = 1;i<=FP_IMAGE_WIDTH - 1;i++) { for(int j = 1;j<=FP_IMAGE_HEIGHT - 1;j++) { if ((P[i,j] == 1) && (i != 0) && (j != FP_IMAGE_HEIGHT - 1) && (j != 0) && (i != FP_IMAGE_WIDTH - 1)) { if ((P[i-1,j-1] == 1) &&( P[i,j-1] == 1) && (P[i+1,j-1] == 1) && (P[i-1,j+1] == 0) && (P[i,j+1] == 0) && (P[i+1,j+1] == 0)) { P[i,j] = 0; //'1 on bottom c++; } else if ((P[i-1,j+1] == 1) && (P[i,j+1] == 1) && (P[i+1,j+1] == 1) && (P[i-1,j-1] == 0) && (P[i,j-1] == 0) && (P[i+1,j-1] == 0)) { P[i,j] = 0; //'1 on top c++; } else if ((P[i-1,j] == 1) && (P[i-1,j - 1] == 1) && (P[i-1,j+1] == 1) && (P[i+1,j] == 0) && (P[i+1,j+1] == 0) && (P[i+1,j-1] == 0)) { P[i,j] = 0; //'1 on left c++; } else if ((P[i+1,j] == 1) && (P[i+1,j-1] == 1) && (P[i+1,j+1] == 1) && (P[i-1,j] == 0) && (P[i-1,j+1] == 0) && (P[i-1,j-1] == 0) ) { P[i,j] = 0; //'1 on right c++; } else if ((P[i-1,j] == 1) && (P[i,j-1] == 1) && (P[i,j+1] == 0) && (P[i+1,j+1] == 0) && (P[i + 1,j] == 0)) { //x00 //110 //x1x P[i,j] = 0; //'1 on Bottem Left c++; } else if ((P[i-1,j] == 1) && (P[i,j+1] == 1) && (P[i,j-1] == 0) && (P[i+1,j-1] == 0) && (P[i+1,j] == 0)) { //x1x //110 //x00 P[i,j] = 0; //'1 on Top Left c++; } else if ((P[i,j+1]== 1) && (P[i+1,j] == 1) && (P[i-1,j] == 0) && (P[i-1,j-1] == 0) && (P[i,j-1] == 0)) { //x1x //011 //00x P[i,j] = 0; //'1 on Top Right c++; } else if ((P[i,j-1] == 1) && (P[i+1,j] == 1) && (P[i-1,j] == 0) && (P[i-1,j+1] == 0) && (P[i,j+1] == 0) ) { //00x //011 //x1x P[i,j] = 0; //'1 on Bottom Right c++; } } }//Next }//Next }//End While }//end ThinningHitAndMiss public void ChaneLinkAlgorithm(int ChainLinkDistance) { //short count1; for(int i = 1;i<= FP_IMAGE_WIDTH - 1;i++) { for (int j = 1 ; j <= FP_IMAGE_HEIGHT - 1;j++) { //change second condition when changeing direction //Horizontal if ((P[i,j] == 1) && (i != FP_IMAGE_WIDTH - 1) && (i != 0) && (j != FP_IMAGE_HEIGHT - 1) && (j != 0)) { if (P[i + 1,j] == 0) { short countX = 0; //count Horizontal Hole while (((i + countX) <= FP_IMAGE_WIDTH - 1) && (countX <= ChainLinkDistance)) { if (((i + countX + 1) <= FP_IMAGE_WIDTH - 1) && ((countX + 1) <= ChainLinkDistance)) { if (P[i + countX + 1,j] == 0) { countX++; } else { break; } } else { break; } } //Fill hole if it is wide enough if ((countX != 0) && ((countX + 1) <= ChainLinkDistance)) { for (int temp = 0; temp<= countX;temp++) { P[i+temp,j] = 1; } } } } //change second condition when changeing direction //Vertical if ((P[i,j] == 1) && (i != FP_IMAGE_WIDTH - 1) && (i != 0) && (j != FP_IMAGE_HEIGHT - 1) && (j != 0)) { if (P[i,j+1] == 0 ) { short countY = 0; //count Horizontal Hole while (((j + countY) <= FP_IMAGE_HEIGHT-1) && (countY <= ChainLinkDistance)) { if (((j + countY + 1) <= FP_IMAGE_HEIGHT-1) && ((countY + 1) <= ChainLinkDistance) ) { //i pu this here bacause it kept on crashing if (P[i,j + countY + 1] == 0) { countY++; } else { break; } } else { break; } } //Fill hole if it is wide enough if ((countY != 0) && (countY + 1 <= ChainLinkDistance)) { for(int temp = 0;temp<= countY;temp++) { P[i,j + temp] = 1; } } } } //change second condition when changeing direction //Vertical Horizontal if ((P[i,j] == 1) && (i != FP_IMAGE_WIDTH - 1) && (i != 0) && (j != FP_IMAGE_HEIGHT - 1) && (j != 0)) { if (P[i + 1,j + 1] == 0) { short countYX = 0; //1 //count Horizontal Hole while ((j + countYX <= FP_IMAGE_HEIGHT-1) && (i + countYX <= FP_IMAGE_WIDTH-1) && (countYX <= ChainLinkDistance)) { if (((j + countYX + 1) <= FP_IMAGE_HEIGHT-1) && ((i + countYX + 1) <= FP_IMAGE_WIDTH-1) && ((countYX + 1) <= ChainLinkDistance)) if (P[i+countYX+1,j + countYX + 1] == 0) { countYX++; } else { break; } else { break; } } //Fill hole if it is wide enough if ((countYX != 0) && (countYX + 1 <= ChainLinkDistance)) { for(int temp = 0 ; temp <= countYX ; temp++) { P[i+temp,j+temp] = 1; } } } } } } } /// /// /// ################################ /// # Extract Origin # /// ################################ /// /// In future i want to use the gradients to classifie the finger print into the 5 different /// catagories which are marked in the FP_CLASS. /// /// This function still needs to improved and somtimes dosen't find the center of the finger print. /// /// The principle in finding the centre is simple , just find the greatest change in the gradient /// bettween two lines and you have your centre. /// /// To find the classification you have to find the average changes in gradients in the different /// sectors (if you divided your picture in 4 using the fingerprint centre as the centre).You should /// then classifie the fingerprint according to this. /// /// private Point getFingerPrintOrigin() { Point m_Point = new Point(); double gradcur=0; double gradprev=0; double gradchangebig=0; double gradchange=0; double graddistancebig=0; double graddistance=0; double prevx=0; double prevy=0; for(int j =50;j<=FP_IMAGE_HEIGHT-50;j++) { for(int i =50;i<=FP_IMAGE_WIDTH-50;i++) { if(P[i,j]==1) { //count surrounding pixels int tc=0; int x1=0; int y1=0; int x2=0; int y2=0; //find surrounding 1s for (int m = -1*FP_TEMPLATE_SEARCH_RADIUS;m<=FP_TEMPLATE_SEARCH_RADIUS;m++) { for (int n = -1*FP_TEMPLATE_SEARCH_RADIUS;n<=FP_TEMPLATE_SEARCH_RADIUS;n++) { if ((m==FP_TEMPLATE_SEARCH_RADIUS)||(m==(-1)*FP_TEMPLATE_SEARCH_RADIUS)||(n==FP_TEMPLATE_SEARCH_RADIUS)||(n==(-1)*FP_TEMPLATE_SEARCH_RADIUS)) { if(P[i+m,j+n] == 1 ) { tc++; if (tc==1) { x1=i+m; y1=j+n; } if (tc==2) { x2=i+m; y2=j+n; } }//end if }//end if }//end for n } //end for m //does all the hard work of finding the greatest change in gradient if(tc==2) { if ((x2-x1)>0)
{
gradcur = (y2-y1)/(x2-x1);
//check to see gradient change by at least 270 degrees

{
gradchange = Math.Abs(gradcur) + Math.Abs(gradprev) ;
graddistance = Math.Abs(i) - Math.Abs(prevx);
if(gradchangebig
{
if (graddistancebig
{
gradchangebig=gradchange;
graddistancebig=graddistance;
m_Point.X=i;//FP_ORIGIN_X =i;
m_Point.Y=j;//FP_ORIGIN_Y =j;
}
}
break;
}
//reset varibles for new checks
gradprev=gradcur;
gradcur=0;
prevx=i;
prevy=j;
}//(x2-x1)>0
}//end if tc==2
}//end if P[x,y]==1
}//end for i
}//end for j
// JOptionPane.showMessageDialog (null,Integer.toString(FP_ORIGIN_X)+";"+Integer.toString(FP_ORIGIN_Y),"getFingerPrintOrigin",JOptionPane.PLAIN_MESSAGE);
return m_Point;
}

private int getFingerPrintClassification()
{
Point m_Point = this.getFingerPrintOrigin();
double gradcur=0;

//stores total gradient of corners
double gradlt=0;
double gradrt=0;
double gradlb=0;
double gradrb=0;

//counts total of each corner gradient
double cgradlt=0;
double cgradrt=0;
double cgradlb=0;
double cgradrb=0;

for(int j =50;j<=FP_IMAGE_HEIGHT-50;j++) { for(int i =50;i<=FP_IMAGE_WIDTH-50;i++) { if(P[i,j]==1) { //count surrounding pixels int tc=0; int x1=0; int y1=0; int x2=0; int y2=0; //find surrounding 1s for (int m = -1*FP_TEMPLATE_SEARCH_RADIUS;m<=FP_TEMPLATE_SEARCH_RADIUS;m++) { for (int n = -1*FP_TEMPLATE_SEARCH_RADIUS;n<=FP_TEMPLATE_SEARCH_RADIUS;n++) { if ((m==FP_TEMPLATE_SEARCH_RADIUS)||(m==(-1)*FP_TEMPLATE_SEARCH_RADIUS)||(n==FP_TEMPLATE_SEARCH_RADIUS)||(n==(-1)*FP_TEMPLATE_SEARCH_RADIUS)) { if(P[i+m,j+n] == 1 ) { tc++; if (tc==1) { x1=i+m; y1=j+n; } if (tc==2) { x2=i+m; y2=j+n; } }//end if }//end if }//end for n } //end for m //does all the hard work of finding the greatest change in gradient if(tc==2) { if ((x2-x1)>0)
{
gradcur = (y2-y1)/(x2-x1);
//check to see gradient change by at least 270 degrees
if ((x2m_Point.Y))
{
gradlt=gradlt+gradcur;
gradlt++;
}
else if ((x2>m_Point.X)&&(y2>m_Point.Y))
{
gradrt=gradrt+gradcur;
gradrt++;
}
else if ((x2
{
gradlb=gradlb+gradcur;
gradlb++;
}
else if ((x2>m_Point.X)&&(y2
{
gradrb=gradrb+gradcur;
gradrb++;
}

}//(x2-x1)>0
}//end if tc==2
}//end if P[x,y]==1
}//end for i
}//end for j
//get average gradient for 4 corners
gradlb=gradlb/cgradlb;
gradrb=gradrb/cgradrb;
gradlt=gradlt/cgradlt;
gradrt=gradrt/cgradrt;
//determin classification according to gradient
//needs work
if ((gradlt>0)&&(gradrt>0)&&(gradlb>0)&&(gradrb>0))
{
return FP_CLASS_WHORL;
}
else if ((gradlt>0)&&(gradrt>0)&&(gradlb>0)&&(gradrb>0))
{
return FP_CLASS_LEFT_LOOP;
}
else if ((gradlt>0)&&(gradrt>0)&&(gradlb>0)&&(gradrb>0))
{
return FP_CLASS_RIGHT_LOOP;
}
else if ((gradlt>0)&&(gradrt>0)&&(gradlb>0)&&(gradrb>0))
{
return FP_CLASS_ARCH;
}
else if ((gradlt>0)&&(gradrt>0)&&(gradlb>0)&&(gradrb>0))
{
return FP_CLASS_ARCH_TENTED;
}
else
{
return 1;
}
// JOptionPane.showMessageDialog (null,Integer.toString(FP_ORIGIN_X)+";"+Integer.toString(FP_ORIGIN_Y),"getFingerPrintOrigin",JOptionPane.PLAIN_MESSAGE);
}


///
///
/// ################################
/// # Extract Template #
/// ################################
///
/// The template will have to be formated according to the ISO standards as set out buy
/// NIST , NIST also has a set of binary pictures to use for examples. This database is used for
/// determaning the FAR(False Acceptance Rate) and FRR (False Rejection Rate)
///
/// First 7 are (elements in array , originx , originy , null , null , null ,null) after that
/// the format is (x,y,r,degree ,number of ends,resultant degree).The size of the array
/// is always pre set.
///
/// There is also future work that needs to be done on genralization , basicly what this means is
/// that you take 3 finger templates , then take the features that are common to all three templeate
/// and you will then come out with a generalized template.This will improve quality of the template.
///
///


public double[] getFingerPrintTemplate()
{
// final int SEARCH_RADIUS = 1;
double x=0;
double y=0;
double r=0;
double d=0;
double[] m_arr = new double[FP_TEMPLATE_MAX_SIZE];

this.ThinningHilditch();
this.ThinningHitAndMiss();
this.ThinningHilditch();
this.ThinningHitAndMiss();

Point origin = this.getFingerPrintOrigin();
m_arr[1]=origin.X;
m_arr[2]=origin.Y;

int c = 7 ;
int previ=0;
int prevj=0;

bool first = true;

//start from 5 units in to avoid detection of edges of finger print and out of bound exceptions
for(int j = 5 ;j<= FP_IMAGE_HEIGHT - 6;j++) { first = true; for( int i =5 ;i<= FP_IMAGE_WIDTH - 6;i++) { if ((c { /* * Must not capture first and last feature because those are the edges of the finger print * and will provide no value to the template. */ if(first == true) { first = false; //cheak to see if previos item in array was aslo end if( (c>7)&&((m_arr[c-6]+origin.X)==previ)&&((m_arr[c-5]+origin.Y)==prevj) )
{
//delete previos featue
m_arr[c--]=0;
m_arr[c--]=0;
m_arr[c--]=0;
m_arr[c--]=0;
m_arr[c--]=0;
m_arr[c--]=0;
}
}
else
{
int tc = 0;
for (int m = -1*FP_TEMPLATE_SEARCH_RADIUS;m<=FP_TEMPLATE_SEARCH_RADIUS;m++) { for (int n = -1*FP_TEMPLATE_SEARCH_RADIUS;n<=FP_TEMPLATE_SEARCH_RADIUS;n++) { if ((m==FP_TEMPLATE_SEARCH_RADIUS)||(m==(-1)*FP_TEMPLATE_SEARCH_RADIUS)||(n==FP_TEMPLATE_SEARCH_RADIUS)||(n==(-1)*FP_TEMPLATE_SEARCH_RADIUS)) { if(P[i+m,j+n] == 1 ) { tc++; }//end if }//end if }//end for n } //end for m //calculate parameters necesary for template if ((tc==1)||(tc==3)) { x = i- origin.X; y = j- origin.Y; r = Math.Sqrt(x*x + y*y); if ((x>0)&&(y>0))
{
d =Math.Atan(y/x);
}

{
d =Math.Atan(y/x) - Math.PI ;
}

{
d =Math.PI + Math.Atan(y/x);
}

{
d =2*Math.PI + Math.Atan(y/x);
}
}

//check to see if point already been captured
bool foundx = false;
bool foundy = false;
for (int m=7;m<=c;m=m+6) { if(m_arr[m+4]==3) { { foundx=true; } { foundy=true; } }//end if }//end for m //1 surrounding 1s if ((tc==1) && (c <= FP_TEMPLATE_MAX_SIZE-6) && (x!=0) && (y!=0) && ((foundx==false)||(foundy==false)) ) { if (P[i-1,j+1] == 1) { m_arr[c++] = x; m_arr[c++] = y; m_arr[c++] = r; m_arr[c++] = d; m_arr[c++] = 1 ; m_arr[c++] = 135 ; } else if (P[i,j+1] == 1) { m_arr[c++] = x; m_arr[c++] = y; m_arr[c++] = r; m_arr[c++] = d; m_arr[c++] = 1 ; m_arr[c++] =90 ; } else if (P[i+1,j+1] == 1) { m_arr[c++] = x; m_arr[c++] = y; m_arr[c++] = r; m_arr[c++] = d; m_arr[c++] = 1 ; m_arr[c++] =45 ; } else if (P[i+1,j] == 1) { m_arr[c++] = x; m_arr[c++] = y; m_arr[c++] = r; m_arr[c++] = d; m_arr[c++] = 1 ; m_arr[c++] = 0 ; } else if (P[i+1,j-1] == 1) { m_arr[c++] = x; m_arr[c++] = y; m_arr[c++] = r; m_arr[c++] = d; m_arr[c++] = 1 ; m_arr[c++] =315 ; } else if (P[i,j-1] == 1) { m_arr[c++] = x; m_arr[c++] = y; m_arr[c++] = r; m_arr[c++] = d; m_arr[c++] = 1 ; m_arr[c++] = 270 ; } else if (P[i-1,j-1] == 1) { m_arr[c++] = x; m_arr[c++] = y; m_arr[c++] = r; m_arr[c++] = d; m_arr[c++] = 1 ; m_arr[c++] = 225 ; } else if (P[i-1, j] == 1 ) { m_arr[c++] = x; m_arr[c++] = y; m_arr[c++] = r; m_arr[c++] = d; m_arr[c++] = 1 ; m_arr[c++] = 180 ; } } else if ((tc>=3)&&(c <= FP_TEMPLATE_MAX_SIZE - 6)&&(x!=0)&&(y!=0)&&((foundx==false)||(foundy==false)) ) { //3 surrounding 1s m_arr[c++] = x; m_arr[c++] = y; m_arr[c++] = r; m_arr[c++] = d; m_arr[c++] = 3; m_arr[c++] = 0; }//if tc>=3
}//end if first
previ=i;
prevj=j;

//306;269
if (((i- origin.X)>=(306 - 4))&&((i- origin.Y)>=(269-4)))
{
if (((i- origin.X)<=(306+4))&&((i- origin.Y)<=(269+4))) { //JOptionPane.showMessageDialog (null,Double.toString(c)+";"+Integer.toString(i)+";"+Integer.toString(j),"My Point",JOptionPane.PLAIN_MESSAGE); } } }//end if that checks for p[x,y]=1 }//end for }//end for //put total size of points collected at 0 in array m_arr[0]=c; return m_arr; }//end getFingerPrintTemplate() public String ConvertFingerPrintTemplateDoubleToString(double[] finger) { String temp=""; for (int i=0;i<=finger.Length-1 ; i++) { temp = temp + finger[i].ToString() +";"; } return temp; } public double [] ConvertFingerPrintTemplateStringToDouble(String finger) { double[] m_finger = new double[FP_TEMPLATE_MAX_SIZE]; int c=-1; String m_double = ""; String temp =""; for (int i=0;i<=finger.Length-1 ; i++) { char[] tempch = finger.ToCharArray(); temp = temp[i].ToString(); if (temp==";") { m_finger[c++] =Double.Parse(m_double); } else { m_double = m_double + temp; } } return m_finger; } /// /// ################################ /// # Matching # /// ################################ /// /// Something to possably look at are /// /// Distance = (X1 -X2)^2 + (Y1 - Y2)^2. The Error_Rating , if a image is is to the left or /// right or even at a angle the distance betwwen matched points will always be the same. /// /// The matching dose account for rotation , thats what the cos and sin are for. /// /// In matching you will never get a 100% match uunless they are exactly the same /// image. 60% is quite good in general ,anything above 55% is considered a match , even /// in other commercial versions. Remember you comparing twod DIFFERENT images then drawing a conclusion of a match. /// /// //cross-corelation algorithm public int Match(double[] finger1,double[] finger2 , int threshold , bool fastmatch) { //compare matrix with all shifted matrixes //must do later. must get the size of the array // JOptionPane.showMessageDialog (null,Double.toString(finger1[0])+";"+Double.toString(finger1[1])+";"+Double.toString(finger2[3]),"Match",JOptionPane.PLAIN_MESSAGE); double matchcount = 0; double matchcounttotal = (finger1[0]-6)/6; double bestmatch =0; double radian = Math.PI/180; bool foundpoint; for (int k = -1*FP_MATCH_POINT_ROTATION_MOVEMENT; k <= FP_MATCH_POINT_ROTATION_MOVEMENT; k++) { for (int i =7; i <= finger1[0]-5; i=i+6) { foundpoint = false; for (int j =7; j <= finger2[0]-5; j=j+6) { if(foundpoint==false) { //compare two points account for rotational , verticle and horizontal shift int resx=0; int resy=0; double x1=0 ; double y1=0; double x2=0; double y2=0; double r=0; double d=0; //find nessasary parameters r =finger2[j+2]; d = finger2[j+3]; x2=finger1[i]; y2=finger1[i+1]; //do angle shift for x x1 = r*Math.Cos(d+(k*radian)); resx = Math.Abs((int)x2 + (int)(-1*x1)); //do angle shift for y y1 = r*Math.Sin(d+(k*radian)); resy = Math.Abs((int)y2 + (int)(-1*y1)); //cheak shift matchs count as match if((FP_MATCH_POINT_DISTANCE_MOVEMENT > resx) && (FP_MATCH_POINT_DISTANCE_MOVEMENT > resy))
{

//cheak if same kind of feature
if(finger1[i+4] == finger2[j+4])
{
//cheak if feature in same direction
// if(((finger1[i+5]-finger2[j+5])<=46)||((finger1[i+5]==0)&&(finger2[j+5]==315))||((finger1[i+5]==0)&&(finger2[j+5]==45))) // { matchcount++; foundpoint = true; //break; // }//cheak if feature in same direction } //cheak if same kind of feature }//end if }//if found }//end for j }//end for i //see if we have a match if ((((matchcount/matchcounttotal)*100)>=threshold)&& (fastmatch == true))
{
//found match
return (int)((matchcount/matchcounttotal)*100);
}
else
{
//not found match
if(matchcount>bestmatch)
{
bestmatch = matchcount;
}
//reset match counter to 0
matchcount=0;
} //end if

}//end for k
return (int)((bestmatch/matchcounttotal)*100);
}//end Match



}//end class
}//end namespace