Face-Recognition-Using-Transfer-Learning

Face Recognition using Transfer Learning on VGG16

In the data science world, we face a lot of situation which require a lot of computational power or resources like RAM, CPU or GPU, etc. to train our models. To cope up with this situation one of the best ways is to use the pre-trained model as in Transfer Learning. This will also help to achieve more accuracy with less amount of data.

VGG16

VGG16 is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper “Very Deep Convolutional Networks for Large-Scale Image Recognition”. The model achieves 92.7% top-5 test accuracy in ImageNet, which is a dataset of over 14 million images belonging to 1000 classes. It was one of the famous models submitted to ILSVRC-2014. It makes the improvement over AlexNet by replacing large kernel-sized filters (11 and 5 in the first and second convolutional layer, respectively) with multiple 3×3 kernel-sized filters one after another. VGG16 was trained for weeks and was using NVIDIA Titan Black GPUs.

Prerequisites

Install following libraires:

conda install tensorflow keras opencv-python pillow numpy

Creating images dataset using opencv through webcam

import cv2
import numpy as np

# Load HAAR face classifier
face_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# Load functions
def face_extractor(img):
    # Function detects faces and returns the cropped face
    # If no face detected, it returns the input image
    
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    faces = face_classifier.detectMultiScale(gray, 1.3, 5)
    
    if faces is ():
        return None
    
    # Crop all faces found
    for (x,y,w,h) in faces:
        cropped_face = img[y:y+h, x:x+w]

    return cropped_face

# Initialize Webcam
cap = cv2.VideoCapture(0)
count = 0

# Collect 1000 samples of your face from webcam input
while True:

    ret, frame = cap.read()
    if face_extractor(frame) is not None:
        count += 1
        face = cv2.resize(face_extractor(frame), (200, 200))
        face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)

        # Save file in specified directory with unique name
        file_name_path = 'C://Users//vivekPC//Desktop//MLOps WS//FRTL//FRsamples//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, (0,255,0), 2)
        cv2.imshow('Face Cropper', face)
        
    else:
        print("Face not found")
        pass

    if cv2.waitKey(1) == 13 or count == 1000: #13 is the Enter Key
        break
        
cap.release()
cv2.destroyAllWindows()      
print("Collecting Samples Complete")

Dataset: