Face Detection Using One-Shot Learning with 49% Face Hidden

Overview

This project aims to detect faces using one-shot learning, even when 49% of the face is occluded. The primary objective is to create a model that can accurately identify faces in challenging scenarios where parts of the face are hidden, such as masks, scarves, or other obstructions. One-shot learning is used to ensure the model can learn from very few examples and generalize well to new individuals.

Features

• Robust Face Detection: Capable of detecting faces with significant occlusions (up to 49% of the face hidden).
• One-Shot Learning: Leverages few-shot techniques to identify new individuals with minimal training data.
• Occlusion Handling: Specifically designed to deal with partially hidden faces, such as masks, sunglasses, or head coverings.
• Real-Time Application: Suitable for real-time face detection in various environments, including surveillance and access control.

Dataset

The dataset for this project includes images of people with different levels of facial occlusion, ensuring a diverse set of conditions:

• Partial Occlusion: Faces covered with masks, scarves, or hands.
• Lighting Conditions: Images in various lighting environments, including indoor and outdoor settings.
• Ethnicity and Gender Diversity: Faces from different ethnicities and genders to improve the model's robustness.

Data Labeling

The dataset will be labeled to mark key facial features and bounding boxes for the visible parts of the face. Labeling should include:

• Bounding Boxes: For the visible portion of the face.
• Key Facial Features: Eyes, nose, and any other visible parts, with a focus on accurately annotating partially visible features.

Requirements

To run the face detection system using one-shot learning, you will need:

• Python 3.8+
• PyTorch for deep learning support
• OpenCV for image processing and video capture
• FaceNet or Siamese Network for one-shot learning
• Albumentations for data augmentation

You can install the required packages using:

pip install torch opencv-python facenet-pytorch albumentations

Model Architecture

The model uses a Siamese network for one-shot learning:

• Feature Extraction: Uses a convolutional neural network (CNN) to extract features from the input images.
• Similarity Measurement: The Siamese network compares two images to determine if they belong to the same person, making it ideal for scenarios with limited training data.
• FaceNet Pre-trained Model: FaceNet is used as a base for extracting facial embeddings, which are then compared using cosine similarity.

Training the Model

1. Prepare the Dataset:

   • Gather images with varying levels of facial occlusion.
   • Label the dataset with bounding boxes for the visible portions of faces.

2. Train the Siamese Network:

   • Train the model to differentiate between pairs of images—whether they belong to the same individual or not.
   • Use contrastive loss to optimize the model's ability to distinguish between similar and dissimilar faces.

   python train_siamese.py --dataset path/to/your/dataset --epochs 50 --batch_size 32

   • Replace path/to/your/dataset with the actual path to your dataset.

Testing and Evaluation

After training, evaluate the model on a test set of images with occlusions:

python test_siamese.py --model path/to/your/best_model.pth --test_data path/to/test_dataset

• Replace path/to/your/best_model.pth with the trained model path.

The model should be able to identify faces correctly, even when up to 49% of the face is hidden.

Real-Time Face Detection

For real-time detection, use a connected webcam to provide video input to the model:

python realtime_face_detection.py --model path/to/your/best_model.pth

• The script will use the webcam feed to detect and identify faces in real-time, including those with occlusions.

Deployment

The trained face detection model can be deployed in different environments:

• Access Control Systems: To identify individuals at entry points, even if they are wearing masks.
• Surveillance Cameras: Monitor areas while detecting individuals with partially covered faces.
• Mobile Applications: Deploy on mobile devices for applications like contactless attendance systems.

Future Work

• Improve model accuracy by incorporating more training data with different types of occlusions.
• Expand to support full facial recognition by including more individuals in the one-shot learning dataset.
• Integrate with cloud-based services for large-scale deployment, including video feeds from multiple locations.

Acknowledgments

• FaceNet for providing the pre-trained face embeddings model.
• OpenCV for providing easy-to-use tools for image and video processing.
• Albumentations for powerful data augmentation techniques that help improve model robustness.

License

This project is licensed under the MIT License. Please see the LICENSE file for more details.