Most people are
good at recognizing the ordinary emotions on other people’s faces. But there
are another set of facial expression that most people are almost entirely
unaware of. In the late 1960s, psychologists discovered that when humans try to
hide their emotions, they often display their real feelings in ‘microexpressions’
that appear and disappear in the blink of an eye. These fleeting facial
expressions have fascinated psychologists and the general public ever since. It
turns out that while most people are entirely oblivious to microexpressions, a
tiny subset of individuals can spot them accurately and use them to tell when
people are hiding their true feelings or when they are downright lying. A
significant industry has grown up that focuses on training people to be better
at recognizing microexpressions. Law enforcement officials and anti-terrorist
agents are often trained in this way in the hope that it can help those spot
individuals who are up to no good.
Whether this training works is the subject of much debate—it may be that
most people do not have the sensory and cognitive skills to catch
microexpressions, regardless of the training they receive. Today, machines
equipped with the best artificial intelligence algorithms can routinely
outperformed humans at object recognition and facial recognition, and have
begun to match them in recognizing expressions and the emotional charge they
carry. That raises an interesting prospect. Could machines soon become better
at recognizing microexpressions than humans?
Today we get an
answer thanks to the work of researchers at the University of Oulu in Finland.
Theys have built and tested the first machine vision system capable of spotting
and recognizing microexpressions and they say that it is already better than
humans at the task. The rapid developments in artificial intelligence in recent
years have come about partly because of improved methods of computing. But
these machines are useless without vast and accurate databases to train them. Their
first task was to create a database of videos showing microexpressions in
realistic conditions. This is easier said than done. Microexpressions tend to
occur when individuals hide their feelings under conditions of relatively high
stakes. Previous work has focused on posed expressions, but various
psychologists have pointed out the limitations of this method, not least of
which is that microexpressions look significantly different to posed
expressions. They tackled this problem
by asking a group of 20 individuals to watch a series of videos designed to
invoke strong emotions among them. These people were given a strong incentive
to avoid showing any emotion during the task: they were told that that they
would have to fill in a long, boring questionnaire explaining any emotions they
did display. As a result, 16 of the 20
individuals produced 164 microexpressions between them, which the team recorded
on a high-speed camera at 100 frames per second. The team linked the emotions
on display to the emotional content of the videos, giving them a gold-standard
database with which to train their machine-learning algorithm.
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