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Iris-Recognition-master/Iris-Recognition-master/FeatureExtraction.py

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+import cv2
+import numpy as np
+import glob
+import math
+import scipy
+from scipy.spatial import distance
+from scipy import signal
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+import matplotlib.pyplot as plt
+import pandas as pd
+from sklearn import metrics
+
+
+#modulating function as defined in paper
+def m(x ,y, f):
+    val = np.cos(2*np.pi*f*math.sqrt(x **2 + y**2))
+    return val
+#spatial filter as defined in paper
+def gabor(x, y, dx, dy, f):
+    gb = (1/(2*math.pi*dx*dy))*np.exp(-0.5*(x**2 / dx**2 + y**2 / dy**2)) * m(x, y, f)
+    return gb
+
+#function to calculate spatial filter over 8x8 blocks
+def spatial(f,dx,dy):
+    sfilter=np.zeros((8,8))
+    for i in range(8):
+        for j in range(8):
+            sfilter[i,j]=gabor((-4+j),(-4+i),dx,dy,f)
+    return sfilter
+
+def get_vec(convolvedtrain1,convolvedtrain2):
+    feature_vec=[]
+    for i in range(6):
+            for j in range(64):
+                #Run 8 by 8 filtered block iteratively over the entire image
+                start_height = i*8
+                end_height = start_height+8
+                start_wid = j*8
+                end_wid = start_wid+8
+                grid1 = convolvedtrain1[start_height:end_height, start_wid:end_wid]
+                grid2 = convolvedtrain2[start_height:end_height, start_wid:end_wid]
+
+                # Channel 1
+                absolute = np.absolute(grid1)
+                # mean
+                mean = np.mean(absolute)
+                feature_vec.append(mean)
+                #deviation
+                std = np.mean(np.absolute(absolute-mean))
+                feature_vec.append(std)
+
+                # Channel 2
+                absolute = np.absolute(grid2)
+                # mean
+                mean = np.mean(absolute)
+                feature_vec.append(mean)
+                #deviation
+                std = np.mean(np.absolute(absolute-mean))
+                feature_vec.append(std)
+
+    return feature_vec
+
+def FeatureExtraction(enhanced):
+    con1=[]
+    con2=[]
+    #get spatial filters
+    filter1=spatial(0.67,3,1.5)
+    filter2=spatial(0.67,4,1.5) 
+    
+    feature_vector=[]
+    
+    for i in range(len(enhanced)):
+        img=enhanced[i]
+        #define a 48x512 region over which the filters are applied
+        img_roi=img[:48,:]
+        
+        filtered1=scipy.signal.convolve2d(img_roi,filter1,mode='same')
+        filtered2=scipy.signal.convolve2d(img_roi,filter2,mode='same')
+        
+        con1.append(filtered1)
+        con2.append(filtered2)
+        fv=get_vec(filtered1,filtered2)
+        feature_vector.append(fv)
+    return feature_vector #each feature vector has a dimension of 1536

Iris-Recognition-master/Iris-Recognition-master/ImageEnhancement.py

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+
+# coding: utf-8
+
+import cv2
+import numpy as np
+import glob
+import math
+from scipy.spatial import distance
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+import matplotlib.pyplot as plt
+import pandas as pd
+from sklearn import metrics
+
+#Equalizes the histogram of the image
+def ImageEnhancement(normalized):
+    enhanced=[]
+    for res in normalized:
+        res = res.astype(np.uint8)
+        im=cv2.equalizeHist(res)
+        enhanced.append(im)
+    return enhanced

Iris-Recognition-master/Iris-Recognition-master/Images/Fig1.png

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+
+# coding: utf-8
+
+import cv2
+import numpy as np
+import glob
+import math
+from scipy.spatial import distance
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+import matplotlib.pyplot as plt
+import pandas as pd
+from sklearn import metrics
+
+def IrisLocalization(images):
+    #convert image to a color image
+    target = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in images]
+    boundary=[] #initialize empty list that will eventually contain all the images with boundaries
+    centers=[] #initialize empty list that will contain the centers of the boundary circles
+    for img in target:
+        
+        draw_img=img
+        
+        # remove noise by blurring the image
+        blur = cv2.bilateralFilter(img, 9,75,75)
+        img=blur
+        
+        #estimate the center of pupil
+        horizontalProjection = np.mean(img,0);
+        verticalProjection = np.mean(img,1);
+        center_x=horizontalProjection.argmin()
+        center_y=verticalProjection.argmin()
+        
+        #recalculate of pupil by concentrating on a 120X120 area
+        centrecrop_x = img[center_x-60:center_x+60]
+        centrecrop_y = img[center_y-60:center_y+60]
+        horizontalProjection = np.mean(centrecrop_y,0);
+        verticalProjection = np.mean(centrecrop_x,0);
+        crop_center_x=horizontalProjection.argmin()
+        crop_center_y=verticalProjection.argmin()
+
+        cimg=img.copy()
+        cv2.circle(cimg,(crop_center_x,crop_center_y),1,(255,0,0),2)
+
+        #apply Canny edge detector on the masked image
+        maskimage = cv2.inRange(img, 0, 70)
+        output = cv2.bitwise_and(img, maskimage)
+        edged = cv2.Canny(output, 100, 220)
+        
+        # Apply Hough transform to find potential boundaries of pupil
+        circles = cv2.HoughCircles(edged, cv2.HOUGH_GRADIENT, 10, 100)
+        
+        #define the center of the pupil
+        a = (crop_center_x,crop_center_y)
+        
+        out = img.copy()
+        min_dst=math.inf
+        for i in circles[0]:
+            #find the circle whose center is closest to the approx center found above
+            b=(i[0],i[1])
+            dst = distance.euclidean(a, b)
+            if dst<min_dst:
+                min_dst=dst
+                k=i
+                
+        #draw the inner boundary
+        cv2.circle(draw_img, (k[0], k[1]), k[2], (255, 0, 0), 3)
+
+        pupil=circles[0][0]
+        radius_pupil = int(k[2])
+        
+        #draw the outer boundary, which is approximately found to be at a distance 53 from the inner boundary 
+        cv2.circle(draw_img, (k[0], k[1]), radius_pupil+53, (255, 0, 0), 3)
+        boundary.append(draw_img)
+        centers.append([k[0],k[1],k[2]])
+    return boundary,centers
+

Iris-Recognition-master/Iris-Recognition-master/Images/Fig2.png

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+# coding: utf-8
+
+import cv2
+import numpy as np
+import glob
+import math
+from scipy.spatial import distance
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+import matplotlib.pyplot as plt
+import pandas as pd
+from sklearn import metrics
+
+def dim_reduction(feature_vector_train,feature_vector_test,components):
+    '''TRAINING'''
+    ft_train=feature_vector_train
+    
+    #get the classes of all training feature vectors
+    y_train=[]
+    for i in range(0,108):
+        for k in range(0,3):
+            y_train.append(i+1)
+    y_train=np.array(y_train)
+    
+    #fit the LDA model on training data with n components
+    sklearn_lda = LDA(n_components=components)
+    sklearn_lda.fit(ft_train,y_train)
+    
+    #transform the traning data
+    red_train=sklearn_lda.transform(ft_train)
+    
+    '''TESTING'''
+    ft_test=feature_vector_test
+    
+    #transform the testing data
+    red_test=sklearn_lda.transform(ft_test)
+    
+    
+    #get a list of predicted values for the testing data to calculate ROC
+    y_pred=sklearn_lda.predict(ft_test)
+    
+    #return transformed training and testing data, and the testing classes and predicted values for ROC
+    return red_train,red_test
+
+
+def IrisMatching(feature_vector_train,feature_vector_test,components,flag):
+    
+    #if flag is 1, we do not need to reduce dimesionality otherwise we call the dim_reduction function
+    if flag==1:
+        red_train=feature_vector_train
+        red_test=feature_vector_test
+        
+    elif flag==0:
+        red_train,red_test=dim_reduction(feature_vector_train,feature_vector_test,components)
+
+
+    arr_f=red_test #test
+    arr_fi=red_train #train
+    
+    index_L1=[]
+    index_L2=[]
+    index_cosine=[]
+    min_cosine=[]
+    
+    #this loop iterates over each test image
+    for i in range(0,len(arr_f)):
+        L1=[]
+        L2=[]
+        Cosine=[]
+        
+        #this loop iterates over every training image - to be compared to each test image
+        for j in range(0,len(arr_fi)):
+            f=arr_f[i]
+            fi=arr_fi[j]
+            sumL1=0 #L1 distance
+            sumL2=0 #L2 distance
+            sumcos1=0
+            sumcos2=0
+            cosinedist=0 #cosine distance
+            
+            #calculate L1 and L2 using the formulas in the paper
+            for l in range(0,len(f)):
+                sumL1+=abs(f[l]-fi[l])
+                sumL2+=math.pow((f[l]-fi[l]),2)
+            
+            
+            #calculate sum of squares of all features for cosine distance
+            for k in range(0,len(f)):
+                sumcos1+=math.pow(f[k],2)
+                sumcos2+=math.pow(fi[k],2)
+                
+            
+            #calculate cosine distance using sumcos1 and sumcos2 calculated above
+            cosinedist=1-((np.matmul(np.transpose(f),fi))/(math.pow(sumcos1,0.5)*math.pow(sumcos2,0.5)))
+            
+            L1.append(sumL1)
+            L2.append(sumL2)
+            Cosine.append(cosinedist)
+        #get minimum values for L1 L2 and cosine distance for each test image and store their index
+        index_L1.append(L1.index(min(L1)))
+        index_L2.append(L2.index(min(L2)))
+        index_cosine.append(Cosine.index(min(Cosine)))
+        min_cosine.append(min(Cosine))
+        
+    match=0
+    count=0
+    
+    #stores final matching - correct(1) or incorrect (0)
+    match_L1=[]
+    match_L2=[]
+    match_cosine=[]
+    match_cosine_ROC=[]
+    
+    #calculating matching of the test set according to the ROC thresholds
+    thresh=[0.4,0.5,0.6]
+    
+    for x in range(0,len(thresh)):
+        match_ROC=[]
+        for y in range(0,len(min_cosine)):
+            if min_cosine[y]<=thresh[x]:
+                match_ROC.append(1)
+            else:
+                match_ROC.append(0)
+        match_cosine_ROC.append(match_ROC)
+        
+    
+    for k in range(0,len(index_L1)):
+        '''count goes from 0 to 3 because we compare the indexes obtained for the first 4 images of the test data
+        to the indexes of the first 3 images of 
+        the train data (for which match is incremented by 3 everytime count exceeds the value of 3)'''
+        if count<4:
+            count+=1
+        else:
+            match+=3
+            count=1
+            
+        '''check if matching is done correctly (1) or not (0) for L1 L2 and cosine distance and accordingly update
+        the arrays match_L1,match_L2,match_cosine'''
+        if index_L1[k] in range(match,match+3):
+                match_L1.append(1)
+        else:
+            match_L1.append(0)
+        if index_L2[k] in range(match,match+3):
+            match_L2.append(1)
+        else:
+            match_L2.append(0)
+        if index_cosine[k] in range(match,match+3):
+            match_cosine.append(1)
+        else:
+            match_cosine.append(0)
+    #reuturns the matching arrays, and test calsses and predicted values to calculate ROC
+    return match_L1,match_L2,match_cosine,match_cosine_ROC
+

Iris-Recognition-master/Iris-Recognition-master/Images/Fig3.png

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+
+# coding: utf-8
+import cv2
+import numpy as np
+import glob
+import math
+from scipy.spatial import distance
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+import matplotlib.pyplot as plt
+import pandas as pd
+from sklearn import metrics
+
+def IrisNormalization(boundary,centers):
+    target = [img for img in boundary]
+    normalized=[]
+    cent=0
+    for img in target:
+        #load pupil centers and radius of inner circles
+        center_x = centers[cent][0]
+        center_y = centers[cent][1]
+        radius_pupil=int(centers[cent][2])
+        
+        iris_radius = 53 # width of space between inner and outer boundary
+    
+        #define equally spaced interval to iterate over
+        nsamples = 360
+        samples = np.linspace(0,2*np.pi, nsamples)[:-1]
+        polar = np.zeros((iris_radius, nsamples))
+        for r in range(iris_radius):
+            for theta in samples:
+                #get x and y for values on inner boundary
+                x = (r+radius_pupil)*np.cos(theta)+center_x
+                y = (r+radius_pupil)*np.sin(theta)+center_y
+                x=int(x)
+                y=int(y)
+                try:
+                #convert coordinates
+                    polar[r][int((theta*nsamples)/(2*np.pi))] = img[y][x]
+                except IndexError: #ignores values which lie out of bounds
+                    pass
+                continue
+        res = cv2.resize(polar,(512,64))
+        normalized.append(res)
+        cent+=1
+    return normalized #returns a list of 64x512 normalized images