Facial coding: a natural, frictionless way to measure
attention and emotional response
The Facial Action Coding System (FACS) was developed by Paul Ekman to categorise human facial movements.
It works as an automated computer system which can categorise human emotions according to changes or movements of the face. Facial expressions are broken down into the individual Action Units that make up a specific expression over time. The output of your collected audience emotions are aggregated and displayed within the dashboard as seven emotion metrics and three proprietary metrics for measuring the emotional experience.
In addition to our emotion metrics, we also track a variety of proprietary metrics. These have been derived through our own research and can be used in conjunction with basic emotions classifiers to gain deeper insights into the content tested.
We’ve developed our own face detector which is more accurate and reliable than the Viola-Jones detector, generally considered the industry standard.
Once the region of the face has been detected, we need to be able to identify the landmark points of the face: the nose, the eyes, the mouth, the eyebrows.
Using all the frames in respondent’s
video, we create a person-dependent
baseline, or mean face shape. By measur
ing their expressions as they deviate from
their ‘neutral’ face, accounts for people
who naturally look more positive or nega
tive and correcting any bias.
To review the accuracy of our motions, we plot the aggregate output of
our metrics against ground truth data – the majority voting of 5 human
annotators who notate frame by frame the emotion they see.
Bad lighting, heavy shadows, thick facial hair,
glasses, typically makes emotion detection more
challenging. However, we use ‘in the wild’ datasets
that employs machine learning that teach our
algorithms to cope with such obstacles.
Get more information on how we collect
respondent data from our tech white paper.
Face detection • Facial landmarks • 3D headpose • Basic emotions • Expressivity • Valence • Attention
Non-exaggerated natural reactions • Complete in-the-wild environment • Video watching in mobile and desktop web browsers • Precision over speed
Emotional events • Emotions duration, volume • Emotions by video segments • Emotions by audience segments • Interest score • Attention volume & quality • Memory & emotional hook
Our white paper covers the science behind our facial coding technology and how we’ve taught machines to recognise emotions, just like humans.