Task-3 : Core Point Detection in Fingerprints

Task-3 : Core Point Detection in Fingerprints

Problem Statement

Core point refers to the centre point of a fingerprint image and is found in the middle of spiral. Task is to find ”Core Point” on a fingerprint image captured through different sensors.

Pre-processing

1. Converted position of Core-Point as a ratio of the image dimensions. This helped in dealing with images of various dimensions. Initially, the regression problem was giving results that were not even in the image dimension. For example, in image of 320*480, the neural network detected the core-point at (2580,1368), which is impossible. On doing this pre-processing, the core-point detected was atleast within image dimension.

2. Converted all images to dimensions to 320*480 pixels using linear interpolation.
3. Converted all images to black-and-white using Adaptive Threshold : This made sure that only data of finger-print was recorded, and the background gradient noise was filtered out.

im = cv2.imread(input_data,cv2.IMREAD_GRAYSCALE)
tho = cv2.adaptiveThreshold(im,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,15,5)

1. Orientation Field using Sobel filters According research, the core-point is located in a vortex with ridges forming a unique orientation pattern around the core-point.

def go(img):
    sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)
    sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=5)
    po = 2 * sobelx * sobely
    go = pow((sobely*sobelx),2)
    a = 0.5*np.arctan2(sobely,sobelx)
    oy = cv2.blur(np.sin(a),(5,5))
    ox = cv2.blur(np.cos(a),(5,5))
    angle = sigmoid(np.arctan2(oy,ox))
    return angle

Model

I have used a simpler version of AlexNet

• Optimizer=Adam(lr=0.0005, decay=0.00001)
• Loss= ‘mean_squared_error’

Model Architecture :

# Layer 1
alexnet = Sequential()
alexnet.add(Conv2D(96, (11, 11), input_shape=(320,480,1),padding='same', kernel_regularizer=l2(0)))
alexnet.add(BatchNormalization())
alexnet.add(Activation('relu'))
alexnet.add(MaxPooling2D(pool_size=(2, 2)))

# Layer 2
alexnet.add(Conv2D(256, (5, 5), padding='same'))
alexnet.add(BatchNormalization())
alexnet.add(Activation('relu'))
alexnet.add(MaxPooling2D(pool_size=(2, 2)))

# Layer 3
alexnet.add(ZeroPadding2D((1, 1)))
alexnet.add(Conv2D(512, (3, 3), padding='same'))
alexnet.add(BatchNormalization())
alexnet.add(Activation('relu'))
alexnet.add(MaxPooling2D(pool_size=(2, 2)))

# Layer 4
alexnet.add(ZeroPadding2D((1, 1)))
alexnet.add(Conv2D(1024, (3, 3), padding='same'))
alexnet.add(BatchNormalization())
alexnet.add(Activation('relu'))

# Layer 5
alexnet.add(ZeroPadding2D((1, 1)))
alexnet.add(Conv2D(1024, (3, 3), padding='same'))
alexnet.add(BatchNormalization())
alexnet.add(Activation('relu'))
alexnet.add(MaxPooling2D(pool_size=(2, 2)))

# Layer 6
alexnet.add(Flatten())
alexnet.add(Dense(3072))
alexnet.add(BatchNormalization())
alexnet.add(Activation('relu'))
alexnet.add(Dropout(0.5))

# Layer 7
alexnet.add(Dense(4096))
alexnet.add(BatchNormalization())
alexnet.add(Activation('relu'))
alexnet.add(Dropout(0.5))

# Layer 8
alexnet.add(Dense(2))
alexnet.add(BatchNormalization())
alexnet.add(Activation('relu'))
return alexnet