Communication involves both verbal, spoken, and nonverbal, unspoken, ways of making sure our message is heard.
When we communicate nonverbally with others, we use facial expressions to get information across. At the bottom line, facial expressions are subtle signals of the larger communication process – while a simple smile can indicate that we approve a message, a scowl most likely signals that we dislike or disagree with the information delivered to us.
Facial expressions are a vital part of our daily communication. To understand what they really are and why they play such an important role for exchanging social information with others, we need to get to the underlying idea of facial expressions first – facial movements.
Pictures and facial movements
In the pictures below, “Lie To Me” star Tim Roth demonstrates the categorical expressions reflecting a specific emotion (all photographs © 2008-2009 Twentieth Century Fox Film).
The facial features that underlie the emotional expression are highlighted in this list of facial expressions pictures:
The idea behind facial expressions: Facial movements
A facial movement is the movement of one or more facial muscles. When we smile, for example, the zygomatic major muscle contracts. This muscle pulls our lips up and back towards our ears.
The mapping between facial movements and facial muscles is not one-to-one. While some facial movements involve the synchronous contraction of one or two muscles, others comprise complex contraction patterns of several muscles.
At the core: Facial expressions and emotions
To the human eye, these muscular contraction patterns are apparent as facial expressions reflecting our current emotional state – angry, happy, sad, fearful, surprised, disgusted or contemptuous.
Built on groundbreaking academic research, these universal facial expressions and the underlying emotional states can be determined via computer-based facial coding engines: Like expert human facial coders, the engines have initially been trained on facial expressions using incredibly large picture and video repositories and databases.
One of the most typical and publicly available databases is the CK+ (“Cohn-Kanade”), developed by Carnegie Mellon University (Lucey et al., 2010). It is a standard benchmark for facial expression recognition that includes both posed and spontaneous expressions. One of the core strengths is that it has been extensively benchmarked in performance compared to expert human coders. Click here to get more information on the CK+.
Knowing the facial expressions of hundreds of thousands of humans across the globe, automatic facial expression engines are able to compare your current facial expression with the “ideal” expression of joy, anger, sadness, fear, surprise, disgust, or contempt. Think of this as a percentage score of how likely your current expression reflects an emotion and can be classified as, say, joy.
If you would like to learn more about computer-based facial expression recognition, reach out to our experts at iMotions.