This project is a comprehensive study and implementation of various face detection algorithms. It is a collaborative effort by a team of students aiming to compare and analyze the performance of different face recognition techniques.
The project includes implementations and evaluations of the following face detection algorithms:
- EigenFaces (Hania)
- FisherFaces (Hania)
- VGG16 (Marta)
- Haar Cascade Classifier (Karol)
- Local Binary Patterns (LBP) (Karol)
- YuNet (Szymon)
- FaceNet (Szymon)
FaceDetectionAlgorithms/
├── dataset/
│ └── (contains datasets for training and testing)
├── eigenface.py
├── fisherface.py
├── vgg16.py
├── haar-cascade-project/
│ └── (Haar Cascade implementation)
├── local-binary-patterns-project/
│ └── (LBP implementation)
├── yunet-face-detection/
│ └── (YuNet implementation)
├── facenet-face-recognition/
│ └── (FaceNet implementation)
├── tools/
│ └── (contains utility tools)
├── .gitignore
├── README.md
└── LICENSE
To get started with the project, clone the repository and install the necessary dependencies:
git clone https://github.com/yourusername/FaceDetectionAlgorithms.gitMake sure you have Python 3.x and pip installed.
To run each algorithm, use the corresponding script. For example:
python eigenface.pyMake sure to update the paths to the datasets in each script if necessary.
- Hania - EigenFaces, FisherFaces
- Marta - VGG16
- Karol - Haar Cascade Classifier, Local Binary Patterns
- Szymon - YuNet, FaceNet