When you’re designing facial capture protocols, there are so many things you need to consider to prevent poor quality data yields and participant fatigue. Factors like…
* what poses you choose
* how you sequence the poses
* how you explain/show the poses, etc.
…make a huge difference in how your sessions can turn out.
How our natural facial asymmetries come through in speech and serve an important role in achieving natural facial movements and authenticity in viseme production.
The depth and amount of lid skin in people with upper lid creases is highly variable! This is one of the many reasons eyeliner users can’t approach lining the same way. Got secrets hidden in there.
If you or your team are using open-source face tracking kits, figuring out what’s what can be challenging. Navigate the ambiguity with the FACS Translation Sheet!
Can we really measure smile authenticity? An exploration of the common assumptions we make about expressions of emotion.
Faces of discomfort often followed headset adjustment – or predicted upcoming adjustments. Bored faces and faces on the contempt spectrum tended to be predictive of undesirable experiences later disclosed during the post-demo interviews. These expressions were not just useful for predicting events. They also served as points for further investigation.
