Face Detection and Recognition using DeepFace (FACE SEEKER)

This project leverages the DeepFace library to perform face detection and recognition. The repository demonstrates how to query an image and find matching faces from a specified image database.

Table of Contents

• Overview
• Features
• Installation
• Usage
• Dependencies
• Results
• Contributing
• Owner

Overview

This project utilizes the DeepFace library to find and display images from a database that match a given query image. It includes:

• A function to detect faces in a database and return matching image paths.
• A visualization tool using Matplotlib to display the query image alongside matching results.

Features

• Face Verification: Verify if two images are of the same person.
• Face Detection: Search for matching faces in a given image database.
• Visualization: Display the query image and matched images using Matplotlib.

Installation

1. Clone the repository:

   git clone https://github.com/Ritikyadav1/face-detection-deepface.git
   cd face-detection-deepface

2. Install the required packages:

   pip install -r requirements.txt

3. Ensure you have the DeepFace library installed:

   pip install deepface

Usage

1. Prepare an image database by storing images in a folder (e.g., /content/database).

2. Run the script to detect and visualize results:

   python main.py

3. Modify the image_path and db_path variables in the script as needed to test with different images.

Code Snippets

Face Detection Function

def detect_faces(image_path, db_path):
    dfs = DeepFace.find(img_path=image_path, db_path=db_path)
    data = dfs[0]
    imgs = data['identity'].tolist()
    return imgs

Visualization Example

import matplotlib.pyplot as plt

# Display the query and found images
plt.figure(figsize=(10, 5))
plt.imshow(query_img)
plt.title('Query Image')
plt.axis('off')

Dependencies

• Python 3.x
• DeepFace
• Matplotlib
• OpenCV
• TensorFlow

Results

This project outputs a list of image paths matching the query and displays them using Matplotlib. The detected images are shown side-by-side with the original query image for easy comparison.

Contributing

Contributions are welcome! Please fork the repository, make your changes, and submit a pull request.

Owner

RITIK YADAV