# face-recognition-server

**Repository Path**: bruno_bai/face-recognition-server

## Basic Information

- **Project Name**: face-recognition-server
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-12-08
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Face recognition with webrtc, websockets and opencv
==========================================
This is a webrtc, websockets and opencv experiment developed during a [athega](http://athega.se) hackday.

How does it work?
-------------------
Frames are captured from the web camera via webrtc and sent to the server over websockets. On the server the frames are processed with opencv and a json response is sent back to the client.

Sample json response:

      {
        "face": {
          "distance": 428.53381034802453,
          "coords": {
            "y": "39",
            "x": "121",
            "height": "137",
            "width": "137"
          },
          "name": "mike"
        }
      }

Everything except `distance` is pretty self explanatory.

* `name` is the predicted name of the person in front of the camera.

* `coords` is where the face is found in the image.

* `distance` is a measurement on how accurate the prediction is, lower is better.

If we can't get a reliable prediction (10 consecutive frames that contains a face and with a distance lower than 1000) we switch over to training mode. In training mode we capture 10 images and send them together with a name back to the server for retraining. After the training has been completed we switch back to recognition mode and hopefully we'll get a more accurate result.

Running it
----------
Make sure the dependencies are met.

* [SQLite](http://www.sqlite.org/)
* [OpenCV](http://opencv.org) with python bindings (I'm using the trunk version)
* [Tornado](http://www.tornadoweb.org)
* [PIL](http://www.pythonware.com/products/pil/)
* [Peewee](https://github.com/coleifer/peewee)
* [scikit-learn](http://scikit-learn.org/stable/) (for running the crossvalidation)

Create the database by issuing the following in the data folder `sqlite3 images.db < ../db/create_db.sql`.

Download the [AT&T face database](http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html) and extract it to `data/images` before the server is started. This is needed to build the initial prediction model.

    cd data
    wget http://www.cl.cam.ac.uk/Research/DTG/attarchive/pub/data/att_faces.tar.Z
    tar zxvf att_faces.tar.Z
    mv att_faces images

Copy `haarcascade_frontalface_alt.xml` from `<path to opencv source>/data/haarcascades/` to the data folder.

Run with `python server.py` and browse to http://localhost:8888 when the model has been trained.