OpenFace 2.2.0: a facial behavior analysis toolkit

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Over the past few years, there has been an increased interest in automatic facial behavior analysis and understanding. We present OpenFace – a tool intended for computer vision and machine learning researchers, affective computing community and people interested in building interactive applications based on facial behavior analysis. OpenFace is the first toolkit capable of facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation with available source code for both running and training the models. The computer vision algorithms which represent the core of OpenFace demonstrate state-of-the-art results in all of the above mentioned tasks. Furthermore, our tool is capable of real-time performance and is able to run from a simple webcam without any specialist hardware.

Multicomp logo

OpenFace was originally developed by Tadas Baltrušaitis in collaboration with CMU MultiComp Lab led by Prof. Louis-Philippe Morency. Some of the original algorithms were created while at Rainbow Group, Cambridge University. The OpenFace library is still actively developed at the CMU MultiComp Lab in collaboration with Tadas Baltršaitis. Special thanks to researcher who helped developing, implementing and testing the algorithms present in OpenFace: Amir Zadeh and Yao Chong Lim on work on the CE-CLM model and Erroll Wood for the gaze estimation work.


For instructions of how to install/compile/use the project please see WIKI


The system is capable of performing a number of facial analysis tasks:

  • Facial Landmark Detection

Sample facial landmark detection image

  • Facial Landmark and head pose tracking (links to YouTube videos)

Multiple Face Tracking Multiple Face Tracking

  • Facial Action Unit Recognition

  • Gaze tracking (image of it in action)

  • Facial Feature Extraction (aligned faces and HOG features)

Sample aligned face and HOG image


If you use any of the resources provided on this page in any of your publications we ask you to cite the following work and the work for a relevant submodule you used.

Overall system

OpenFace 2.0: Facial Behavior Analysis Toolkit Tadas Baltrušaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency, IEEE International Conference on Automatic Face and Gesture Recognition, 2018

Facial landmark detection and tracking

Convolutional experts constrained local model for facial landmark detection A. Zadeh, T. Baltrušaitis, and Louis-Philippe Morency. Computer Vision and Pattern Recognition Workshops, 2017

Constrained Local Neural Fields for robust facial landmark detection in the wild Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency. in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.

Eye gaze tracking

Rendering of Eyes for Eye-Shape Registration and Gaze Estimation Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling in IEEE International Conference on Computer Vision (ICCV), 2015

Facial Action Unit detection

Cross-dataset learning and person-specific normalisation for automatic Action Unit detection Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson in Facial Expression Recognition and Analysis Challenge, IEEE International Conference on Automatic Face and Gesture Recognition, 2015

Commercial license

For inquiries about the commercial licensing of the OpenFace toolkit please visit

Final remarks

I did my best to make sure that the code runs out of the box but there are always issues and I would be grateful for your understanding that this is research code and a research project. If you encounter any problems/bugs/issues please contact me on github or by emailing me at [email protected] for any bug reports/questions/suggestions. I prefer questions and bug reports on github as that provides visibility to others who might be encountering same issues or who have the same questions.


Copyright can be found in the Copyright.txt

You have to respect dlib, OpenBLAS, and OpenCV licenses.

Furthermore you have to respect the licenses of the datasets used for model training –


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