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FACIAL EXPRESSION | Joy (stylized smile 2)

11/1/2016

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SMILES : For Animators, Less can be More
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Notice the gleaming smile on the woman behind the speakers!
Why do stylized expressions work so well?  The whole world laughs along with Mickey Mouse, and grins along with Bugs Bunny and Tweety Bird, even though their faces are visually a long ways from their human counterparts.  We immediately and unambiguously identify with their emotions, and relate to them as though they were flesh-and-blood actors.   Small children "get it," as well as octogenarians.
This recent campaign photograph contains a detail that reveals the principle behind our relating so deeply to non-literal (a.k.a “stylized”) smiles.  Look at the out-of-focus crowd behind Donald Trump and his companion.  Our attention is immediately drawn to the blond woman in the center, and you ignore her neighbors.  What catches our eye is her beautiful, broad smile.  Although that seems obvious, look again at what you are really noticing.  Her face is tiny, very blurry, low-contrast, and so low-resolution that her exact identity would be hard to determine.  But we have absolute certainty about her expression, and we even assign it an intensity level, and have confidence in its authenticity and warmth.  (Trump’s expression is much more ambiguous, by comparison.)  
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Clearly, level of detail and pictorial clarity is not an impediment to recognizing or judging a smile.  This is a skill, and one that is common to all of us.  Like so much else that is part of our innate toolkit of non-verbal communication, this ability allows us to function successfully in daily interactions, where judging the attitude of a stranger in a quick encounter might mean the difference (back in the day) between life and death.  We can’t simply be good at doing this quickly and accurately at close range and with good lighting; we need to be able to do this at dusk, in the rain, and in the jungle (for example).
Smiles – the most important of all the expressions and the one with the most complexity – sends a visual signal that cannot be confused with any other expression, nor is it similar to faces with no emotion.  This is a key reason why it has made the cut as a primal expression.  Look at the people around the smiling woman – whatever their feelings might be, we don’t get any cues that we can identify, and we don’t pay attention.  The clarity and high contrast of this smile pattern (cheek shadows, shape of the white teeth in contrast to the dark lips) lends itself to quick, minimal detail, identification.  Artists, by extension, are able to exploit our low-resolution smile recognition ability, to create non-literal smiles with simplified and stylized features, like Bugs and Lightning McQueen (below).  We already have the tools to identify those smiles, and we are already primed to respond. 

Speaking of Mickey, he’s not pictured in this blog post for a reason – his smile uses an entirely different anatomical cue, another brilliant Disney innovation.  More about that in my next post.
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Bugs Bunny's smile is the most inventive of the three stylized examples shown here. While the other two hew fairly close to their human counterparts, the artist who created Bugs took the normal row of upper teeth and fused them into mega-teeth, then exaggerated the always-present corner voids (see photo left).  This is a very good use of anatomical cues to create something very non-anatomical that still gets the message across.
Credits: Top - Trump|Pence campaign photo, Mississippi; Bottom - Bugs Bunny animated cartoon character, created by staff of Leon Schlesinger Productions (later Warner Bros. Cartoons), featured in Looney Tunes and Merry Melodies; Lightning McQueen, animated character from Cars,  2006, directed and co-written by John Lasseter, produced by Pixar Animation Studios and released by Walt Disney Pictures; Tooth and toothbrush from stock image; Photo of smiling face from "22 Facts about the Human Body," on SneakHype.com. 
For earlier post on Stylized Smiles, go here.

To view the complete FACE BLOG, go here.
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