README.md

### Model Card: **PRNet 3D Face Reconstruction**

**Model Name**: `PRNet_3D`

#### Model Description:
PRNet is a deep learning model for **3D face reconstruction** from a single 2D image. This model regresses a 3D position map and reconstructs dense facial landmarks from 2D inputs. The fine-tuned version of PRNet has been optimized to handle facial images more robustly in the provided domain.

- **Architecture**: Position Map Regression Network (PRNet)
- **Base Model**: Pre-trained PRNet (before fine-tuning)
- **Training Data**: Custom dataset of 2D facial images and corresponding 3D meshes.
- **Purpose**: The model is used for forensic investigations, facial recognition, and 3D modeling from 2D images.

#### Model Details:
- **Training**: Fine-tuned using Google Colab with TensorFlow 1.x and the PRNet architecture. The model was trained on a specific dataset of 2D face images and optimized for 3D face reconstruction.
- **Outputs**: The model outputs a `.obj` file that contains the 3D mesh representation of the input 2D image.

#### Usage:
This model is intended for **3D face reconstruction** tasks. It takes a 2D facial image and outputs a 3D `.obj` file of the reconstructed face.

##### Example:
```python
from your_project_module import PRN
import numpy as np
from skimage.io import imread

prn = PRN(is_dlib=False)  # Initialize the model without dlib
image = imread('path_to_image.jpg')
image = resize(image, (256, 256))  # Resize image to 256x256

# Process and generate 3D vertices
pos = prn.net_forward(image / 255.0)
vertices = prn.get_vertices(pos)
```

#### Intended Use:
- **Forensic Investigations**: Reconstruction of faces from low-quality images for law enforcement or identification purposes.
- **3D Modeling**: Generates 3D models from 2D images for entertainment, games, or medical applications.
- **Facial Recognition**: Can be used for generating 3D facial profiles for use in recognition systems.

#### Limitations and Risks:
- **Accuracy in Reconstruction**: The accuracy of the 3D reconstruction depends heavily on the quality and resolution of the input 2D image.
- **Bias and Dataset Limitations**: Since the model is fine-tuned on a specific dataset, there may be biases or limitations when applied to other types of facial structures or ethnicities.
- **Sensitivity to Image Quality**: Low-quality images may produce less accurate 3D models or fail entirely to reconstruct.

#### How to Cite:
If you use this model, please cite the original PRNet authors and mention the fine-tuned adjustments:
```
@misc{prnet_3d_finetuned,
  title={PRNet 3D Face Reconstruction Finetuned Model},
  author={Mostafa Aly},
  year={2024},
  howpublished={\url{https://huggingface.co/your-hf-username/PRNet_3D}},
}
```

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