Enhanced Real-Time Face and Eye Detection Using Custom Haar Cascades in OpenCV

Overview

This repository features a custom-trained model for real-time face and eye detection utilizing OpenCV in Python. Developed by SampathiNaman, this project integrates machine learning with classical computer vision techniques to deliver robust and efficient detection.

Components

• A custom-trained Haar Cascade models for detecting faces and eyes.
• main.py script for face and eye detection.

Methodology

• Data Collection: Gather a diverse dataset of images featuring faces and eyes in various environments, poses, and lighting conditions.
• Preprocessing: Apply techniques such as resizing, normalization, and data augmentation to enhance dataset quality and diversity.
• Model Training: Train custom Haar Cascade classifiers using annotated datasets for precise face and eye detection.
• Optimization: Fine-tune models to improve detection accuracy and minimize false positives.
• Integration: Embed trained Haar Cascade models into the OpenCV framework for seamless real-time detection.
• Evaluation: Test performance on benchmark datasets and real-world scenarios to assess accuracy, speed, and robustness.
• Deployment: Optimize for real-time applications, ensuring compatibility with various hardware environments.

Key Features

• Custom-Trained Haar Cascade Model: Specifically trained for detecting faces and eyes with improved accuracy on custom datasets.
• Versatile Detection: Supports detection in static images, video files, and real-time through webcam.
• Easy Deployment: Simple Python script to demonstrate the model's usage across different media types.

Technology

• Programming Language: Python
• Libraries: OpenCV, NumPy
• Machine Learning Framework: scikit-learn
• Tools: Haarcascade XML files, Anaconda, Jupyter Notebooks
• Hardware: Compatible with CPUs and GPUs for deployment on various platforms

Getting Started

Prerequisites

Ensure you have the following installed:

• Python 3.x
• OpenCV library

You can install OpenCV using pip:

pip install opencv-python

Installation

1. Clone the repository:

git clone https://github.com/SampathiNaman/Face-and-eye-detection.git

2. Navigate to the project directory:

cd Face-and-eye-detection

Usage

Run the main.py script to use the model:

python main.py [options]

Options

• --image IMAGE_PATH: Specify the path to an image file for face and eye detection.
• --video VIDEO_PATH: Specify the path to a video file for face and eye detection.
• If no options are provided, the script will default to using the webcam for real-time detection.

Example Usage:

To detect faces and eyes in an image:

python main.py --image ./path-to-image/image.jpg

To detect faces and eyes in a video:

python main.py --video ./path-to-video/video.mp4

To run real-time detection via webcam:

python main.py

Datasets Documentation

For a comprehensive overview of the datasets used in this project, and their applications within the project, please refer to our detailed dataset documentation. This documentation includes information on both the positive and negative datasets used in training and testing of the models.

Contributing

We welcome contributions! If you have suggestions for improving the model or extending its functionalities, please follow these steps:

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.

Acknowledgments

• We extend our gratitude to the developers of the Cascade Training GUI application for simplifying the custom Haar Cascade training process.
• Special thanks to Innovate for their invaluable support and guidance during this internship project.