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https://github.com/m-pektas/identitysimilarity
This repository can help researchers who want to use face recognition in their research.
https://github.com/m-pektas/identitysimilarity
deep-learning deep-neural-networks face-recognition identity-loss loss loss-functions pytorch
Last synced: 2 days ago
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This repository can help researchers who want to use face recognition in their research.
- Host: GitHub
- URL: https://github.com/m-pektas/identitysimilarity
- Owner: m-pektas
- Created: 2022-04-07T19:38:23.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-02-27T18:17:45.000Z (11 months ago)
- Last Synced: 2024-11-15T07:54:08.756Z (about 2 months ago)
- Topics: deep-learning, deep-neural-networks, face-recognition, identity-loss, loss, loss-functions, pytorch
- Language: Jupyter Notebook
- Homepage:
- Size: 526 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Identity Similarity
This repository can help researchers who want to use face recognition in their research. You can easily implement powerful face recognition models in your project. I motivated by
[LPIPS](https://github.com/richzhang/PerceptualSimilarity) for this repository. The models are borrowed from [Insigtface](https://github.com/deepinsight/insightface/tree/master/recognition/arcface_torch).
**Warning :** Please, be careful when chosing your criterion. Lower is more similar in MSE while higher is more similar in CosineSimilarity.
## Supported Metrics
- MSE
- L1
- Cosine
## Supported Models
- ms1mv3_arcface_r50
- glint360k_cosface_r100
## Usage
### 1. Training with preprocessed dataset.
In this case, we assume that you have aligned images using a keypoint template and you want to calculate identity similarity between two aligned images or a image and a saved identity vector.
```python
import torch
import numpy as np
from idsim import IdentitySimilarity
idsim = IdentitySimilarity()
template = np.array([[35.066223, 34.23266],
[84.1586, 33.96113],
[59.768444, 62.152763],
[39.60066, 90.89288],
[80.255, 90.66802]], dtype=np.float32)
idsim.set_ref_point(template)
# dummy variables
v1 = torch.rand(1, 512)
im1 = torch.rand(5, 3, 128, 128)
# useful functions
sim_v2v = idsim.forward_v2v(v1, v1)
sim_im2im = idsim.forward_img2img(im1, im1)
sim_v2im = idsim.forward_v2img(v1, im1)
print("\nsim_v2v :", sim_v2v, "\nsim_im2im :", sim_im2im, "\nsim_v2im :", sim_v2im)
```
### 2. Face recognition
In this case, Idsim can caculate identity similarity of your images.
```python
import cv2
from idsim import IdentitySimilarity
idsim = IdentitySimilarity(criterion="Cosine")
img1 = cv2.imread("a.jpg")
img2 = cv2.imread("b.jpg")
v1 = idsim.extract_identity(img1)
v2 = idsim.extract_identity(img2)
sim = idsim.forward_v2v(v1,v2)
print("Similarity :", sim)
```
**Note:** You can check the [proving_differentiability.ipynb](proving_differentiability.ipynb) for an example training.
## Contributing
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are **greatly appreciated**.
1. Fork the Project
1. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
1. Run `make style && make quality` in the root repo directory, to ensure code quality.
1. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
1. Push to the Branch (`git push origin feature/AmazingFeature`)
1. Open a Pull Request