clmtrackr

clmtrackr is a javascript library for fitting facial models to faces in videos or images. It currently is an implementation of constrained local models fitted by regularized landmark mean-shift, as described in Jason M. Saragih's paper. clmtrackr tracks a face and outputs the coordinate positions of the face model as an array, following the numbering of the model below:

Reference - Overview

The library provides some generic face models that were trained on the MUCT database and some additional self-annotated images. Check out clmtools for building your own models.

For tracking in video, it is recommended to use a browser with WebGL support, though the library should work on any modern browser.

For some more information about Constrained Local Models, take a look at Xiaoguang Yan's excellent tutorial, which was of great help in implementing this library.

Examples

• Tracking in image
• Tracking in video
• Face substitution
• Face masking
• Realtime face deformation
• Emotion detection
• Caricature

Usage

Download the minified library clmtrackr.js, and include it in your webpage.

/* clmtrackr libraries */

The following code initiates the clmtrackr with the default model (see the reference for some alternative models), and starts the tracker running on a video element.

You can now get the positions of the tracked facial features as an array via

getCurrentPosition()

You can also use the built in function

draw()

See the complete example here.

Development

First, install node.js with npm.

In the root directory of clmtrackr, run

npm install

npm run build

clmtrackr.js

clmtrackr.module.js

build

To test the examples locally, you need to run a local server. One easy way to do this is to install

http-server

npm install -g http-server

http-server

https://localhost:8080/examples

License

clmtrackr is distributed under the MIT License