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Emotion recognition for Minor Research In Emerging Technologies
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Measuring Heritage Emotions

This project uses Python, DLib, OpenCV and Facial Landmarks to recognize emotions from images/videos.


According to Waag Society and the Research Group Crossmedia, recent studies have shown that young adults (<21) are hard to reach when it comes down to (cultural) heritage. In particular Waag Society is researching how cultural heritage institutions can connect to these groups and how heritage objects can be relevant to (young) people. Both parties believe that a better understanding of the emotions people have, is very important to learn more about the way people value cultural heritage. Therefore, Waag Society and the Research Group Crossmedia asked students of the HvA to design an interactive tool that captures young adults’ emotions and enables them to discuss these emotions with their peers when looking at (cultural) heritage.

Getting Started

In order to get the program working, quite a number of prerequisites must be met. Most of these involve external libraries.

A lot of what is described here, can be read back in more detail on Paul van Gent's blog (see also the citations).


  • Python 2.7
  • Dlib (we used: dlib 19.7)
  • Boost (we used: 1.65.1)
  • SKLearn (pip install sklearn)
  • Numpy (pip install numpy)
  • OpenCV (we used: 2.4.13)

You might want to use a virtualenvironment when setting up everything below.

Building Dlib is fairly straightforward. You can read the details on this on Paul van Gent's blog, or you can google it. It basically requires the C++ Boost library to be build, and then you can build Dlib and install it for Python. If everything installed correctly, but you still get an ImportError when importing dlib, running sudo ldconfig might solve this.

SKlearn will already be installed if you're using Anaconda. If not, pip install sklearn will do the trick. pip install numpy is sufficient for numpy as well.

OpenCV gave a lot of trouble when building from source. So here are a couple of notes / tips that helped make the build succesful:

  • If you want to build OpenCV version 2.4.9, you might run into an error during building pertaining to the face_detector.cpp, you should try building a newer version of OpenCV (2.4.13 for example).
  • The build might fail on ffmpeg. In this case, you should build ffmpeg from source before building OpenCV. For example:
cd ffmpeg-<version>
./configure --enable-nonfree --enable-pic --enable-shared
sudo make install

Warning This will override any version of ffmpeg you might already have installed. Any software that relied on ffmpeg might break as a cause of this (ex. mpv).

  • You might need to run cmake with -D ENABLED_PRECOMPILED_HEADER=OFF (see also this github issue)

What worked for us eventually:

  • Build ffmpeg from source.
  • Next use CMake:
cd opencv-<version>
mkdir build
cd build
  • Check the output of CMake to ensure there is ffmpeg support.
  • Make and make install: make && make install

Once the build is successful, check in a Python interpreter session that you can do import cv2. If this is not the case, you might need to manually copy or symlink to your python2.7/lib folder:

cp /path/to/opencv/build/lib/ /path/to/virtualenv/lib/python2.7/
OR to symlink:
ln -s /path/to/opencv/build/lib/ /path/to/virtualenv/lib/python2.7/

Open a new Python interpreter session, and try import cv2 again.


Once all the above requirements are met, you can try it out. Some functionality, such as sorting and preparing of the dataset, requires there to be a dataset. We used the Cohn-Kanade extended (CK+) dataset.

Example usage:

$ python -m emotionreader --help
usage: emotionreader [-h] [-V] {sort,prepare-dataset,train,webcam} ...

An open-source emotion detection system built for the minor Research in
Emerging Technologies at the University of Applied Sciences Amsterdam.

positional arguments:
  {sort,prepare-dataset,train,webcam}     the action to perform
    sort                sort the CK+ dataset
    prepare-dataset     prepare the dataset by detecting faces and cutting
                        them to size
    train               train the model
    webcam              start real timedetection from the command line
optional arguments:
    -h, --help          show this help message and exit
    -V, --version       show program's version number and exit

Each action also has a help function. For example:

$ python -m emotionreader webcam --help
usage: emotionreader webcam [-h] [-d DIMENSIONS] [-l]

optional arguments:
    -h, --help          show this help message and exit
    -d DIMENSIONS, --dimensions DIMENSIONS
                        the width and height to start the webcam with
    -l, --landmarks     draw the facial landmarks on the frame


  • van Gent, P. (2016). Emotion Recognition Using Facial Landmarks, Python, DLib and OpenCV. A tech blog about fun things with Python and embedded electronics. Retrieved from:
  • Kanade, T., Cohn, J. F., & Tian, Y. (2000). Comprehensive database for facial expression analysis. Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition (FG'00), Grenoble, France, 46-53.
  • Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010). The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression. Proceedings of the Third International Workshop on CVPR for Human Communicative Behavior Analysis (CVPR4HB 2010), San Francisco, USA, 94-101.
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