Face Recognition Attendance System

Overview

This Jupyter notebook implements an Attendance System using face recognition. The system utilizes the FaceNet model for generating embeddings and a triplet loss function for training. The key components include loading and preprocessing images, training the FaceNet model, and implementing the attendance system.

Dependencies

• Python 3.x
• TensorFlow
• PyTorch
• OpenCV
• PIL (Pillow)

Model and Training

The FaceNet model is used for facial recognition. The training involves creating triplets of anchor, positive, and negative images, and the model is trained using a triplet loss function. The training process is detailed in the notebook.

Attendance System

The attendance system is designed to recognize faces and mark attendance based on detected faces. The model is loaded, and images are preprocessed before being fed into the model for recognition.

Usage

1. Clone the repository.
2. Install the required dependencies using pip install -r requirements.txt.
3. Run the Jupyter notebook to train the FaceNet model and implement the attendance system.

Note

• Ensure that the dataset contains images of individuals with unique identities.
• Adjust hyperparameters and settings according to specific requirements.

Acknowledgments

This project is inspired by the Face Recognition and FaceNet model. Credits to the authors and contributors of these technologies.

Feel free to explore, modify, and use this code for educational and practical purposes.