18 November 2015

Machine Vision Algorithm Recognizes Hidden Facial Expressions

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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