FaceNet works by first detecting faces in images using a deep convolutional neural network (CNN). Once faces are detected, the model extracts facial features and maps them into a high-dimensional space called an embedding space.
In this embedding space, faces belonging to the same person are clustered closely together, while faces of different individuals are far apart. This allows for accurate discrimination between different faces based on their unique features.
The key innovation of FaceNet lies in its use of a triplet loss function during training. This loss function encourages the model to minimize the distance between embeddings of the same person's face while maximizing the distance between embeddings of different individuals. This results in face embeddings that are highly discriminative and suitable for face recognition tasks.
- Implementation of the FaceNet model for face recognition.
- Generation of high-quality face embeddings.
- Accurate recognition of faces based on their unique features.
(https://arxiv.org/abs/1503.03832)
Contributions are welcome!
This project is licensed under the MIT License - see the LICENSE file for details.