https://github.com/fosetorico/face_matching_app
End-to-End Face Recognition project that matches faces with Hollywood celebrities using the power of MTCNN for face detection as well as VGG-Face for feature extraction and recognition
https://github.com/fosetorico/face_matching_app
computer-vision deep-learning facial-recognition mtcnn streamlit tensorflow vggface
Last synced: 7 months ago
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End-to-End Face Recognition project that matches faces with Hollywood celebrities using the power of MTCNN for face detection as well as VGG-Face for feature extraction and recognition
- Host: GitHub
- URL: https://github.com/fosetorico/face_matching_app
- Owner: fosetorico
- License: mit
- Created: 2024-12-15T14:29:25.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-12-16T20:14:21.000Z (10 months ago)
- Last Synced: 2025-01-06T08:37:35.823Z (9 months ago)
- Topics: computer-vision, deep-learning, facial-recognition, mtcnn, streamlit, tensorflow, vggface
- Language: Python
- Homepage:
- Size: 9.27 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# End-to-End Face Recognition and Matching
# STEPS to run this project:
You can also use others images
## STEP 01:
Clone the repository
```bash
git clone https://github.com/fosetorico/face_matching_app
```
## STEP 02:
Create an environment
```bash
conda create -n venv python=3.7 -y
```
## STEP 03:
Install the requirements
```bash
pip install -r requirements.txt
```
## STEP 04:
Download the data from the link and keep it in your project directory. Make sure all the sub-folder are kept in one folder called data, like that
https://www.kaggle.com/datasets/vishesh1412/celebrity-face-image-dataset
or any Image dataset of your choice
## STEP 05:
Just execute this command one time if you are not changing the data
```bash
python run.py
```
## STEP 06:
Now to start the webapp run the following command
```bash
streamlit run app.py
```
Yes!! Now you can now start Face Matching 🙂