Commited minutiae

2026-02-13 13:17:41 +01:00
parent 0ee74cbcb8
commit 9595675cdb
40 changed files with 4125 additions and 0 deletions
@@ -0,0 +1,172 @@
+"""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},
+    }
+"""
+
+import sys,os
+sys.path.append(os.path.realpath('../FineNet'))
+
+from keras.optimizers import Adam
+from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard
+from keras.callbacks import ReduceLROnPlateau
+from keras.preprocessing.image import ImageDataGenerator
+from FineNet_model import FineNetmodel, plot_confusion_matrix
+
+import numpy as np
+import os
+from sklearn.metrics import confusion_matrix
+from datetime import datetime
+
+
+os.environ["CUDA_VISIBLE_DEVICES"] = '2'
+os.environ['KERAS_BACKEND'] = 'tensorflow'
+
+
+output_dir = '../output_FineNet/'+datetime.now().strftime('%Y%m%d-%H%M%S')
+
+# Prepare model model saving directory.
+save_dir = os.path.join(os.getcwd(), output_dir)
+log_dir = os.path.join(os.getcwd(), output_dir + '/logs')
+
+# Training parameters
+batch_size = 32
+epochs = 200
+num_classes = 2
+
+# Subtracting pixel mean improves accuracy
+subtract_pixel_mean = True
+
+# Model size, patch
+model_type = 'patch224batch32'
+
+
+# =============== DATA loading ========================
+
+train_path = '../Dataset/train/'
+test_path = '../Dataset/validate/'
+
+input_shape = (224, 224, 3)
+
+# Using data augmentation technique for training
+datagen = ImageDataGenerator(
+        # set input mean to 0 over the dataset
+        featurewise_center=False,
+        # set each sample mean to 0
+        samplewise_center=False,
+        # divide inputs by std of dataset
+        featurewise_std_normalization=False,
+        # divide each input by its std
+        samplewise_std_normalization=False,
+        # apply ZCA whitening
+        zca_whitening=False,
+        # randomly rotate images in the range (deg 0 to 180)
+        rotation_range=180,
+        # randomly shift images horizontally
+        width_shift_range=0.5,
+        # randomly shift images vertically
+        height_shift_range=0.5,
+        # randomly flip images
+        horizontal_flip=True,
+        # randomly flip images
+        vertical_flip=True)
+
+train_batches = datagen.flow_from_directory(train_path, target_size=(input_shape[0], input_shape[1]), classes=['minu', 'non_minu'], batch_size=batch_size)
+# Feed data from directory into batches
+test_gen = ImageDataGenerator()
+test_batches = test_gen.flow_from_directory(test_path, target_size=(input_shape[0], input_shape[1]), classes=['minu', 'non_minu'], batch_size=batch_size)
+
+
+# =============== end DATA loading ========================
+
+
+
+def lr_schedule(epoch):
+    """Learning Rate Schedule
+    """
+    lr = 0.5e-2
+    if epoch > 180:
+        lr *= 0.5e-3
+    elif epoch > 150:
+        lr *= 1e-3
+    elif epoch > 60:
+        lr *= 5e-2
+    elif epoch > 30:
+        lr *= 5e-1
+    print('Learning rate: ', lr)
+    return lr
+
+
+
+
+#============== Define model ==================
+
+model = FineNetmodel(num_classes = num_classes,
+                     pretrained_path = '../Models/FineNet.h5',
+                     input_shape=input_shape)
+
+# Save model architecture
+#plot_model(model, to_file='./modelFineNet.pdf',show_shapes=True)
+
+model.compile(loss='categorical_crossentropy',
+              optimizer=Adam(lr=lr_schedule(0)),
+              metrics=['accuracy'])
+#model.summary()
+
+#============== End define model ==============
+
+
+#============== Other stuffs for loging and parameters ==================
+model_name = 'FineNet_%s_model.{epoch:03d}.h5' % model_type
+if not os.path.isdir(save_dir):
+    os.makedirs(save_dir)
+if not os.path.isdir(log_dir):
+    os.makedirs(log_dir)
+
+filepath = os.path.join(save_dir, model_name)
+
+
+# Show in tensorboard
+tensorboard = TensorBoard(log_dir=log_dir, histogram_freq=0, write_graph=True, write_images=False)
+
+# Prepare callbacks for model saving and for learning rate adjustment.
+checkpoint = ModelCheckpoint(filepath=filepath,
+                             monitor='val_acc',
+                             verbose=1,
+                             save_best_only=True)
+
+lr_scheduler = LearningRateScheduler(lr_schedule)
+
+lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),
+                               cooldown=0,
+                               patience=5,
+                               min_lr=0.5e-6)
+
+callbacks = [checkpoint, lr_reducer, lr_scheduler, tensorboard]
+
+#============== End other stuffs  ==================
+
+# Begin training
+model.fit_generator(train_batches,
+                    validation_data=test_batches,
+                    epochs=epochs, verbose=1,
+                    callbacks=callbacks)
+
+
+
+# Plot confusion matrix
+score = model.evaluate_generator(test_batches)
+print 'Test accuracy:', score[1]
+predictions = model.predict_generator(test_batches)
+test_labels = test_batches.classes[test_batches.index_array]
+
+cm = confusion_matrix(test_labels, np.argmax(predictions,axis=1))
+cm_plot_labels = ['minu','non_minu']
+plot_confusion_matrix(cm, cm_plot_labels, title='Confusion Matrix')