https://github.com/hamarshehmhmd/face_trackerv2
This is an update for te Face_Tracker Project that takes a picture of the user and authenticates if the user in the picture is the same person on the camera
https://github.com/hamarshehmhmd/face_trackerv2
opencv python
Last synced: 3 months ago
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This is an update for te Face_Tracker Project that takes a picture of the user and authenticates if the user in the picture is the same person on the camera
- Host: GitHub
- URL: https://github.com/hamarshehmhmd/face_trackerv2
- Owner: hamarshehmhmd
- License: mit
- Created: 2023-08-04T15:06:32.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-04-28T00:35:16.000Z (about 2 years ago)
- Last Synced: 2025-10-07T09:53:28.008Z (9 months ago)
- Topics: opencv, python
- Language: Python
- Homepage:
- Size: 220 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Face and Eye Detection Project
This is a simple Python script that uses the OpenCV and face_recognition libraries to perform face and eye detection on live video feed from a webcam. The script also checks if a detected face matches a reference image provided by the user.
## Requirements
- Python 3
- OpenCV (`cv2`)
- Numpy (`numpy`)
- face_recognition (`face_recognition`)
## Installation
You can install the required libraries using pip:
```pip install opencv-python numpy face-recognition```
## Usage
1. Clone or download the project files to your local machine.
2. Ensure you have a reference image that you want to use for face matching. Place this image on your desktop and update the `reference_image_path` variable in the script with the correct file name.
3. Run the script:
```python face_eye_detection.py```
4. The script will open a new window showing the video feed from your webcam with rectangles drawn around detected faces and eyes. If a face matches the reference image, it will be labeled as "Identity Match" in green; otherwise, it will be labeled as "Human" in red.
5. To quit the script, press the 'q' key in the video feed window.
## Troubleshooting
1. If you encounter any issues related to the face_recognition library, make sure you have installed it correctly. You can find installation instructions on the [face_recognition GitHub page](https://github.com/ageitgey/face_recognition).
2. If the script is not detecting faces or eyes properly, you can try adjusting the `scaleFactor` and `minNeighbors` parameters in the `face_cascade.detectMultiScale()` and `eye_cascade.detectMultiScale()` functions, respectively.
## Limitations
- The accuracy of face matching heavily depends on the quality and similarity of the reference image with the faces in the video feed.
- This script may not be suitable for real-world face recognition applications that require high accuracy and security.
## Credits
- This project uses the OpenCV library for face and eye detection.
- The face recognition is powered by the face_recognition library.
## License
This project is licensed under the [MIT License](LICENSE).