"""Code for FineNet in paper "Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge" at ICB 2018 https://arxiv.org/pdf/1712.09401.pdf If you use whole or partial function in this code, please cite paper: @inproceedings{Nguyen_MinutiaeNet, author = {Dinh-Luan Nguyen and Kai Cao and Anil K. Jain}, title = {Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge}, booktitle = {The 11th International Conference on Biometrics, 2018}, year = {2018}, } """ from __future__ import absolute_import from __future__ import division from keras.models import Model from keras.layers import Activation, AveragePooling2D, BatchNormalization, Concatenate, Conv2D, Dense, GlobalAveragePooling2D from keras.layers import Input, Lambda, MaxPooling2D from keras.applications.imagenet_utils import _obtain_input_shape from keras import backend as K import matplotlib.pyplot as plt import numpy as np import itertools def preprocess_input(x): """Preprocesses a numpy array encoding a batch of images. """ return keras.applications.imagenet_utils.preprocess_input(x, mode='tf') def conv2d_bn(x, filters, kernel_size, strides=1, padding='same', activation='relu', use_bias=False, name=None): """Utility function to apply conv + BN. """ x = Conv2D(filters, kernel_size, strides=strides, padding=padding, use_bias=use_bias, name=name)(x) if not use_bias: bn_axis = 1 if K.image_data_format() == 'channels_first' else 3 bn_name = None if name is None else name + '_bn' x = BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x) if activation is not None: ac_name = None if name is None else name + '_ac' x = Activation(activation, name=ac_name)(x) return x def inception_resnet_block(x, scale, block_type, block_idx, activation='relu'): """Inception-ResNet block. """ if block_type == 'block35': branch_0 = conv2d_bn(x, 32, 1) branch_1 = conv2d_bn(x, 32, 1) branch_1 = conv2d_bn(branch_1, 32, 3) branch_2 = conv2d_bn(x, 32, 1) branch_2 = conv2d_bn(branch_2, 48, 3) branch_2 = conv2d_bn(branch_2, 64, 3) branches = [branch_0, branch_1, branch_2] elif block_type == 'block17': branch_0 = conv2d_bn(x, 192, 1) branch_1 = conv2d_bn(x, 128, 1) branch_1 = conv2d_bn(branch_1, 160, [1, 7]) branch_1 = conv2d_bn(branch_1, 192, [7, 1]) branches = [branch_0, branch_1] elif block_type == 'block8': branch_0 = conv2d_bn(x, 192, 1) branch_1 = conv2d_bn(x, 192, 1) branch_1 = conv2d_bn(branch_1, 224, [1, 3]) branch_1 = conv2d_bn(branch_1, 256, [3, 1]) branches = [branch_0, branch_1] else: raise ValueError('Unknown Inception-ResNet block type. ' 'Expects "block35", "block17" or "block8", ' 'but got: ' + str(block_type)) block_name = block_type + '_' + str(block_idx) channel_axis = 1 if K.image_data_format() == 'channels_first' else 3 mixed = Concatenate(axis=channel_axis, name=block_name + '_mixed')(branches) up = conv2d_bn(mixed, K.int_shape(x)[channel_axis], 1, activation=None, use_bias=True, name=block_name + '_conv') x = Lambda(lambda inputs, scale: inputs[0] + inputs[1] * scale, output_shape=K.int_shape(x)[1:], arguments={'scale': scale}, name=block_name)([x, up]) if activation is not None: x = Activation(activation, name=block_name + '_ac')(x) return x def FineNetmodel(num_classes = 2, pretrained_path = None, input_shape = None): """Create FineNet architecture. """ # Determine proper input shape input_shape = _obtain_input_shape( input_shape, default_size=299, min_size=139, data_format=K.image_data_format(), require_flatten=False, weights=pretrained_path) img_input = Input(shape=input_shape) # Stem block: 35 x 35 x 192 x = conv2d_bn(img_input, 32, 3, strides=2, padding='valid') x = conv2d_bn(x, 32, 3, padding='valid') x = conv2d_bn(x, 64, 3) x = MaxPooling2D(3, strides=2)(x) x = conv2d_bn(x, 80, 1, padding='valid') x = conv2d_bn(x, 192, 3, padding='valid') x = MaxPooling2D(3, strides=2)(x) # Mixed 5b (Inception-A block): 35 x 35 x 320 branch_0 = conv2d_bn(x, 96, 1) branch_1 = conv2d_bn(x, 48, 1) branch_1 = conv2d_bn(branch_1, 64, 5) branch_2 = conv2d_bn(x, 64, 1) branch_2 = conv2d_bn(branch_2, 96, 3) branch_2 = conv2d_bn(branch_2, 96, 3) branch_pool = AveragePooling2D(3, strides=1, padding='same')(x) branch_pool = conv2d_bn(branch_pool, 64, 1) branches = [branch_0, branch_1, branch_2, branch_pool] channel_axis = 1 if K.image_data_format() == 'channels_first' else 3 x = Concatenate(axis=channel_axis, name='mixed_5b')(branches) # 10x block35 (Inception-ResNet-A block): 35 x 35 x 320 for block_idx in range(1, 11): x = inception_resnet_block(x, scale=0.17, block_type='block35', block_idx=block_idx) # Mixed 6a (Reduction-A block): 17 x 17 x 1088 branch_0 = conv2d_bn(x, 384, 3, strides=2, padding='valid') branch_1 = conv2d_bn(x, 256, 1) branch_1 = conv2d_bn(branch_1, 256, 3) branch_1 = conv2d_bn(branch_1, 384, 3, strides=2, padding='valid') branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x) branches = [branch_0, branch_1, branch_pool] x = Concatenate(axis=channel_axis, name='mixed_6a')(branches) # 20x block17 (Inception-ResNet-B block): 17 x 17 x 1088 for block_idx in range(1, 21): x = inception_resnet_block(x, scale=0.1, block_type='block17', block_idx=block_idx) # Mixed 7a (Reduction-B block): 8 x 8 x 2080 branch_0 = conv2d_bn(x, 256, 1) branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding='valid') branch_1 = conv2d_bn(x, 256, 1) branch_1 = conv2d_bn(branch_1, 288, 3, strides=2, padding='valid') branch_2 = conv2d_bn(x, 256, 1) branch_2 = conv2d_bn(branch_2, 288, 3) branch_2 = conv2d_bn(branch_2, 320, 3, strides=2, padding='valid') branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x) branches = [branch_0, branch_1, branch_2, branch_pool] x = Concatenate(axis=channel_axis, name='mixed_7a')(branches) # 10x block8 (Inception-ResNet-C block): 8 x 8 x 2080 for block_idx in range(1, 10): x = inception_resnet_block(x, scale=0.2, block_type='block8', block_idx=block_idx) x = inception_resnet_block(x, scale=1., activation=None, block_type='block8', block_idx=10) # Final convolution block: 8 x 8 x 1536 x = conv2d_bn(x, 1536, 1, name='conv_7b') # Classification block x = GlobalAveragePooling2D(name='avg_pool')(x) x = Dense(num_classes, activation='softmax', name='predictions')(x) inputs = img_input # Create model model = Model(inputs, x, name='FineNet') # Load weights if pretrained_path != None: print 'Loading FineNet weights from %s'%(pretrained_path) model.load_weights(pretrained_path) return model def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues): """ This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. """ plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=45) plt.yticks(tick_marks, classes) if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] print("Normalized confusion matrix") else: print('Confusion matrix, without normalization') print(cm) thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, cm[i, j], horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label') plt.show()