Charles Darwin was the first to intuit that humans and other mammals reveal emotions through facial expression. In “The Expression of the Emotions in Man and Animals” (1872), he suggested that mammals reveal emotions in their faces in universal ways.
In the early ‘60s, Paul Ekman began traveling to other countries to test whether at least some facial expressions were consistent despite geographical differences and cultures. He found that individuals from different regions correctly interpreted expressions in photos of other groups. In 1972, Ekman published research confirming that many facial expressions of emotions are universally found in different cultures and identified seventh of them: anger, happiness, surprise, disgust, sadness, fear and contempt (added years later).
In 1978, Ekman introduced a system for classifying facial movements, the FACS (Facial Action Coding System).
How does it work?
The FACS system is used by attributing a combination of codes corresponding to certain facial micro-movements (Action Units) made by the subject. Furthermore, it is possible to identify an intensity of the movement and the combination of these movements can lead to a subsequent decoding or a “translation” of the code into a predominantly emotional meaning.
Specifically, the algorithm used for facial coding extracts the main features of the face (eyes, lips, nose, cheekbones, etc…) and analyzes the movement, shape and composition of these regions to identify the action units. Therefore, it is possible to trace tiny facial muscle movements in the face of individuals and translate them into universal expressions using facial expression recognition.
Why does it matter?
Facial coding, falling within the techniques and methods of analyzing non-verbal behavior, provides information on the subjects’ spontaneous and often unfiltered reactions by automatically recording and analyzing facial micro-expressions.
It is easy to understand that the use of these algorithms have multiple applications. One of the most important is in marketing where they can be very useful for better understanding customers and how they react to certain stimuli in order to create better performing and personalized campaigns.
Naturally, this implies a better return on investment (ROI) and, obviously, the relationship with customers will also be impacted positively and in the long run.
Learn more about Emotion AI and Facial Coding
Collectively, face coding has the potential to revolutionize a wide range of industries. If you would like to learn more about how Facial Coding contributes to Emotion AI, you can learn more by reading our Emotion AI Glossary.
Check out this “Emotion AI Glossary” to learn more about Emotion AI. You can also see it in action by scheduling a demo with the Emotiva team.