Building a Face Recognition System with Logistic Regression

In this follow-along tutorial, we're diving into the exciting world of face recognition using Logistic Regression. By the end of this article, you'll have a solid understanding of how to build a simple face recognition model using Python, and more importantly, why this technology is significant in today's world.

Face recognition systems are widely used in security systems, unlocking personal devices, and even in social media applications for tagging friends. Understanding how these systems work can open up numerous opportunities in tech development and data science.

Table of Contents

1. Introduction

2. Setting Up the Environment

3. Loading the Dataset

4. Exploring the Dataset

5. Preparing the Data

6. Training the Model

7. Evaluating the Model

8. Making Predictions

9. Conclusion

1. Introduction

Face recognition technology has revolutionized the way we interact with various devices and applications. From unlocking your phone to tagging friends on social media, face recognition is everywhere. In this tutorial, we'll build a simple face recognition system using Logistic Regression, which is a powerful yet straightforward classification algorithm.

Benefits of Learning Face Recognition:

• Security Enhancements: Understand the underlying technology in security systems.

• Automation: Learn how to automate tasks involving face detection and recognition.

• Career Opportunities: Enhance your skill set in data science and machine learning.

2. Setting Up the Environment

First, we need to import the necessary libraries. These include libraries for data manipulation, visualization, and machine learning.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import fetch_lfw_people

These libraries are essential for handling data, splitting datasets, training models, and visualizing results.

3. Loading the Dataset

We'll be using the Labeled Faces in the Wild (LFW) Dataset, which contains images of famous people. This dataset is perfect for training and testing our face recognition model.

lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
print(lfw_people.DESCR)

The dataset description gives us an overview of the data, including the number of images and the task definitions.

4. Exploring the Dataset

Let's explore the dataset by printing the names of the people in the dataset

for name in lfw_people.target_names:
    print(name)

Next, we'll look at the data and target arrays to understand the structure of our dataset.

data = lfw_people.data
targets = lfw_people.target
target_names = lfw_people.target_names

index = 3
print(data[index])
print(targets[index])
print(target_names[targets[index]])

We'll also visualize one of the images to get a sense of what the data looks like.

data_reshaped = np.reshape(data[index], (50, 37))
plt.imshow(data_reshaped, cmap=plt.cm.gray)
plt.show()

5. Preparing the Data

Splitting the data into training and testing sets is crucial for evaluating our model's performance.

X_train, X_test, y_train, y_test = train_test_split(data, targets, test_size=0.15)
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)

6. Training the Model

We can now train our Logistic Regression model on the training data.

logisticReg = LogisticRegression(max_iter=1000)
logisticReg.fit(X_train, y_train)
print(logisticReg)

7. Evaluating the Model

To evaluate the model, we'll test it on a few examples from the test set and visualize the results.

index = 5
test_data = X_test[index]
test_data_reshaped = np.reshape(test_data, (50, 37))
plt.imshow(test_data_reshaped, cmap=plt.cm.gray)
plt.show()

test_data_reshaped_for_predict = np.reshape(test_data, (1, 1850))
pred_label_val_test_data = logisticReg.predict(test_data_reshaped_for_predict)
print(target_names[pred_label_val_test_data])

We can also check the overall accuracy of our model on the test set.

print(logisticReg.score(X_test, y_test))

8. Making Predictions

Let's make predictions on a few more test samples to see how well our model performs.

index = 10
test_data = X_test[index]
test_data_reshaped_for_predict = np.reshape(test_data, (1, 1850))
pred_label_val_test_data = logisticReg.predict(test_data_reshaped_for_predict)

pred_name = lfw_people.target_names[pred_label_val_test_data]
print("Pred Label:", pred_label_val_test_data, "Pred Name:", pred_name)
print("Actual Label:", y_test[index], "; Actual Name:", target_names[y_test[index]])

To get a detailed performance report, we use the classification report from sklearn.

from sklearn.metrics import classification_report

y_pred = logisticReg.predict(X_test)

print(classification_report(y_test, y_pred, target_names=target_names))

We can also inspect the shape of the model coefficients and predicted probabilities.

print(logisticReg.coef_.shape)
print(logisticReg.predict_proba(X_test).shape)

9. Conclusion

In this article, we built a simple face recognition system using Logistic Regression. This technology is not only fascinating but also incredibly useful in various applications. By understanding the basics of face recognition, you can explore more advanced techniques and contribute to developing secure and efficient systems.

Importance of Face Recognition Technology:

• Enhanced Security: Critical for biometric security systems.

• Convenience: Simplifies user interactions with devices and applications.

• Data Analysis: Useful in social media, law enforcement, and personalized marketing.

References and Resources:

• LFW Dataset

• Scikit-learn Documentation

• Logistic Regression Explained

• Face Recognition Applications

This tutorial should have provided you with a good foundation in face recognition technology. Keep experimenting and exploring the vast world of machine learning and data science!