https://github.com/tempdata73/face_verification
Real time face tracking and verification
https://github.com/tempdata73/face_verification
detection facenet tracking verification
Last synced: 7 months ago
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Real time face tracking and verification
- Host: GitHub
- URL: https://github.com/tempdata73/face_verification
- Owner: tempdata73
- Created: 2019-05-17T19:49:38.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-03-01T00:30:41.000Z (over 5 years ago)
- Last Synced: 2025-01-22T13:47:56.670Z (9 months ago)
- Topics: detection, facenet, tracking, verification
- Language: Python
- Homepage:
- Size: 64.2 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 4
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Metadata Files:
- Readme: README.md
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README
# Real time face tracking and verification
Opencv-tensorflow-mxnet implementation of face tracking and verification on real time using either CPU or GPU.
The pretrained models used for face verification were obtained from [David Sandberg's github repo](https://github.com/davidsandberg/facenet)
which are tensorflow implementations of [FaceNet](https://arxiv.org/abs/1503.03832).
Instead of using Viola and Jones' face detector, this repository uses a [implementation of the MTCNN model](https://github.com/YYuanAnyVision/mxnet_mtcnn_face_detection) for both face detection and tracking initialization. It is also used for database creation.
(the original paper can be found [here](https://kpzhang93.github.io/MTCNN_face_detection_alignment/paper/spl.pdf))
## Compatibility
The code has been tested using python 3.7.1 under 18.10 Ubuntu.
## Download
1. Clone this repo and cd to the 'models' directory.
2. Download either Facenet model from [Sandberg's repo](https://github.com/davidsandberg/facenet).
3. Run `python prepare_facenet.py path/to/facenet.zip` and cd to parent directory.
## Demo
1. Cd to the 'src' directory.
2. Run `python main.py demo/avengers.mp4 demo/user_data.csv demo/embeddings.npy`.
## Guide
The program consists of three parts: video file (webcam feed or downloaded video); user metadata and embeddings (both created from the *create_database.py* script). User metadata and embeddings derive from an image folder that contains all of the users to be identified. As a prerequisite you should have both video file and the images folder. Take into account that the image filename of a user will be used as his username.
1. Cd to the 'src' directory.
2. Run `python create_database.py output/user_data.csv output/embeddings.npy path/to/images/` to create embedddings.
3. Run `python main.py video_file.mp4 output/user_data.csv output/embeddings.npy` for face tracking and verification. If you wish to keep track of time a user appears in the video, run *screentime.py* instead of *main.py*.