LivenessDetection.py

The LivenessDetection.py file controls the LivenessDetection model processing which differentiates real faces from flat images.

Imports

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv3D, MaxPooling3D

• Keras: Necessary for deep learning functions to process the model

Methods

The getModel() method is used to process the data within a model so that is more usable. This is done through a Sequential model with several layers to correctly process the data.

def getModel():
    model = Sequential()
    model.add(Conv3D(32, kernel_size=(3, 3, 3),
                     activation='relu',
                     input_shape=(24, 100, 100, 1)))
    model.add(Conv3D(64, (3, 3, 3), activation='relu'))
    model.add(MaxPooling3D(pool_size=(2, 2, 2)))
    model.add(Conv3D(64, (3, 3, 3), activation='relu'))
    model.add(MaxPooling3D(pool_size=(2, 2, 2)))
    model.add(Conv3D(64, (3, 3, 3), activation='relu'))
    model.add(MaxPooling3D(pool_size=(2, 2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(2, activation='softmax'))

    return model

The getModelPred() method is used to simply initialize and load the model with its respective weights.

def getModelPred():
    model = getModel()
    model.load_weights("Model/model.h5")
    return model