Face Recognition using Transfer Learning

  • Problem Statement: Create a project using transfer learning solving various problems like Face Recognition, Image Classification, using existing Deep Learning models like VGG16, VGG19, ResNet, etc.

What is Transfer Learning ?

  • In ML , Transfer Learning is just a technique in which the knowledge gained by a model while training with a dataset is used for training it with some other dataset and here the knowledge is the weights calculated by the model.

In ML world , there are multiple pre-trained models available like VGG , MobileNet , ResNet , Inception etc. And in this project i have used MobileNet.

  • So let’s get started with the project.
  1. First i prepared dataset using cv2. I ran this code twice for collecting images of two persons.This is the code.I have used harcascade for detecting the face part so that i can crop the face part automatically.
import cv2
import numpy as np
face_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# Function to detect face and give cropped face as output
def face_extractor(img):
faces = face_classifier.detectMultiScale(img, 1.3, 5)
if faces is ():
return None
for (x,y,w,h) in faces:
cropped_face = img[y:y+h, x:x+w]
return cropped_face
# Capturing frames using webcame <0 for default, 1 for external>
cap = cv2.VideoCapture(0)
count = 0
while True:
ret, frame = cap.read()
if face_extractor(frame) is not None:
count += 1
face = cv2.resize(face_extractor(frame), (200, 200))
# Save file in specified directory with unique name
file_name_path = 'C://Users//91721//Desktop//ml_task4//images//image' + str(count) + '.jpg'
cv2.imwrite(file_name_path, face)
# Put count on images and display live count
cv2.putText(face, str(count), (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255), 3)
cv2.imshow('Face Cropper', face)
else:
print("Error finding face")
pass
if cv2.waitKey(1) == 13 or count == 100:
break
cap.release()
cv2.destroyAllWindows()
print("Data collected !!")
  • In this i have collected 100 images for training and 20 images for validation but this much images are not enough to train the model as it will not able to recognize the features of our face precisely and also we get less accuracy. So for increasing our dataset we can use Augmentation which basically rotate and flip our images to create new images and by this we can create thousands of images from just 100 images.
  • Now we have to load the MobileNet model and for this we will download the weights of this model from Imagenet and since we have to do transfer learning and don’t have to train the whole model again , so we will freeze all the layers of the model and then create 4 more new layers and train these layers only.
  • Now after all this we have to load our dataset and then train the model.
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, GlobalAveragePooling2D
from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras.optimizers import RMSprop
from keras.callbacks import ModelCheckpoint, EarlyStopping
from keras.preprocessing.image import ImageDataGenerator
# Load MobileNet model
from keras.applications import MobileNet
# MobileNet was designed to work on 224 x 224 pixel input images sizes
img_rows, img_cols = 224, 224
# Re-loads the MobileNet model without the top or FC layers
MobileNet = MobileNet(weights = 'imagenet',
include_top = False,
input_shape = (img_rows, img_cols, 3))
# freezing the last 4 layers
# Layers are set to trainable as True by default
for layer in MobileNet.layers:
layer.trainable = False

# function that returns FC Head
def lw(bottom_model, num_classes):
"""creates the top or head of the model that will be
placed ontop of the bottom layers"""
top_model = bottom_model.output
top_model = GlobalAveragePooling2D()(top_model)
top_model = Dense(1024,activation='relu')(top_model)
top_model = Dense(1024,activation='relu')(top_model)
top_model = Dense(512,activation='relu')(top_model)
top_model = Dense(num_classes,activation='softmax')(top_model)
return top_model

num_classes = 2
FC_Head = lw(MobileNet, num_classes)
model = Model(inputs = MobileNet.input, outputs = FC_Head)
# Loading Dataset
train_data_dir = 'C://Users//91721//Desktop//ml_task4//'
validation_data_dir = 'C://Users//91721//Desktop//ml_task4//'
# Data augmentaiton
train_datagen = ImageDataGenerator(
rescale=1./255,
rotation_range=45,
width_shift_range=0.3,
height_shift_range=0.3,
horizontal_flip=True,
fill_mode='nearest')

validation_datagen = ImageDataGenerator(rescale=1./255)

# setting the batch size (typically on most mid tier systems we'll use 16-32)
batch_size = 32

train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_rows, img_cols),
batch_size=batch_size,
class_mode='categorical')

validation_generator = validation_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_rows, img_cols),
batch_size=batch_size,
class_mode='categorical')

checkpoint = ModelCheckpoint("task3/myface_model.h5",
monitor="val_loss",
mode="min",
save_best_only = True,
verbose=1)
earlystop = EarlyStopping(monitor = 'val_loss',
min_delta = 0,
patience = 3,
verbose = 1,
restore_best_weights = True)
# putting call backs into a callback list
callbacks = [earlystop, checkpoint]
# using a very small learning rate
model.compile(loss = 'categorical_crossentropy',
optimizer = RMSprop(lr = 0.001),
metrics = ['accuracy'])
# Enter the total number of training and validation samples here
nb_train_samples = 200
nb_validation_samples = 40
# only training 3 EPOCHS
epochs = 3
batch_size = 16
history = model.fit_generator(
train_generator,
steps_per_epoch = nb_train_samples // batch_size,
epochs = epochs,
callbacks = callbacks,
validation_data = validation_generator,
validation_steps = nb_validation_samples // batch_size)
  • Here i have trained my model with 3 epochs but you can do more epochs as more is the epochs more will be the accuracy and also i have saved my model , so that in future if we want this model then we don’t have to train the model again.
  • Now the model gets trained and we can use it to predict about the person.
from keras.models import load_modelclassifier = load_model('task3/mymodel_face.h5')import os
import cv2
import numpy as np
from os import listdir
from os.path import isfile, join
person_dict = {"[0]": "Abhishek kumar",
"[1]": "Adarsh" }
person_dict_n = {"Abhi": "Abhishek kumar",
"Adarsh": "Adarsh" }
def draw_test(name, pred, im):
face = person_dict[str(pred)]
BLACK = [0,0,0]
expanded_image = cv2.copyMakeBorder(im, 80, 0, 0, 100 ,cv2.BORDER_CONSTANT,value=BLACK)
cv2.putText(expanded_image, face , (20, 60) , cv2.FONT_HERSHEY_SIMPLEX,1, (0,0,255), 2)
cv2_imshow(name, expanded_image)
def getRandomImage(path):
"""function loads a random images from a random folder in our test path """
folders = list(filter(lambda x: os.path.isdir(os.path.join(path, x)), os.listdir(path)))
random_directory = np.random.randint(0,len(folders))
path_class = folders[random_directory]
print("Class - " + person_dict_n[str(path_class)])
file_path = path + path_class
file_names = [f for f in listdir(file_path) if isfile(join(file_path, f))]
random_file_index = np.random.randint(0,len(file_names))
image_name = file_names[random_file_index]
return cv2.imread(file_path+"/"+image_name)
for i in range(0,10):
input_im = getRandomImage("C://Users//91721//Desktop//ml_task4//test//")
input_original = input_im.copy()
input_original = cv2.resize(input_original, None, fx=0.5, fy=0.5, interpolation = cv2.INTER_LINEAR)

input_im = cv2.resize(input_im, (224, 224), interpolation = cv2.INTER_LINEAR)
input_im = input_im / 255.
input_im = input_im.reshape(1,224,224,3)

# Get Prediction
res = np.argmax(classifier.predict(input_im, 1, verbose = 0), axis=1)

# Show image with predicted class
draw_test("Prediction", res, input_original)
cv2.waitKey(0)
cv2.destroyAllWindows()

Link for all the codes :- https://github.com/skabhi001/ml-task4.git

Thanks, hope you guys will like my article. if you have any suggestion or query will free to ask.

#Happylearning #keepsharing #devops #integration

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