Recently, Machine Learning engineers have been pushing the boundaries of computer vision experiments to the boundaries of practical applications in the real-world. The application of facial recognition has taken on particular importance due to the ethical considerations of using such a powerful tool. Despite the concerns, facial recognition technology has been used to enhance security, build automated check-ins and more.
In this blog post, we will discuss how to use Machine Learning to turn computer vision into a facial recognition tool. We will use Python and its various open source Machine Learning libraries to generate a facial recognition system.
The first step to building a facial recognition system is to create a database of faces. By having a collection of features from faces, we can compare the features from newly presented faces and recognize them. To generate a database, we must first collect images of individuals and their features. Generally, the features we collect include the height and width of the face, the distance between the eyes, the length of the nose, and the size of the mouth.
Once the images have been collected, we can use OpenCV to perform feature extraction, or facial recognition. OpenCV is a powerful open-source tool that allows us to detect faces, eyes, and mouths in an image. We start by detecting faces using OpenCV’s Haar Cascade feature. We then perform feature extraction using the Histogram of Oriented Gradients (HOGs) technique. HOGs makes it possible to detect parts in an image and to recognize objects in images.
Once the features have been extracted, it’s time to train the Machine Learning model. We will be using artificial neural networks (ANNs) as the underlying model. ANNs are a type of supervised learning algorithm, meaning that we must provide labeled data for our training data set. This means that we have to label each of our collected images with the person that it belongs to.
Once labeled, the data is given to an ANN for training. ANNs are composed of neurons, each one “examining” different parts of the image. During training, the ANN learns to identify different features in the image. We can also adjust the parameters of the ANN to see how it affects the accuracy of the model.
Once the model is trained, it’s time to test it. To test the accuracy of the model, we divide our data set into a training set and a testing set. This way, we can evaluate how well the model is performing without overfitting to the training data.
We can also use a confusion matrix to evaluate the performance of the model. A confusion matrix is a matrix that compares the ground “truth” with the predicted results. This allows us to see exactly where the model is making mistakes and where it is correctly identifying faces.
Once the model is trained and tested, it is time to deploy it. The most popular way to deploy Machine Learning models is through web services. This allows us to expose our models to the world and, possibly, to create an API.
Using an API, we can easily integrate our model with other applications. With an API, we can allow users to upload images of people to our facial recognition system and it will return the name of the individual in the photo.
In this blog post, we discussed how to use Machine Learning to turn computer vision into a facial recognition tool. We discussed how to generate a database of faces, how to train a model, how to test the model, and how to deploy the model.
Facial recognition systems are powerful tools that are being constantly improved. By following the steps outlined in this post, Machine Learning engineers can build their own facial recognition systems and deploy them to the world.
code snippet:
# Import packages
import cv2
import numpy as np
# Perform feature extraction
img = cv2.imread('face.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Detect faces
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
# Extract features
for (x,y,w,h) in faces:
face_features = gray[y:y+h, x:x+w]
hog = cv2.HOGDescriptor()
feature = hog.compute(face_features)
features.append(feature)
# Label features
for feature in features:
if feature == ‘Person A’:
labels.append(‘Person A’)
elif feature == ‘Person B’:
labels.append(‘Person B’)