Much of this kind of computation is based on the so-called Facial Action Coding System (FACS), a method developed by Paul Elkman, a specialist in facial micro-expressions, during the 1970s and 1980s. FACS decomposes emotional expressions into their distinct facial elements. In other words, it breaks down emotions to specific sets of facial muscles and movements: the widening of the eyes, the elevation of the cheeks, the dropping of the lower lip, and so on. FACS is used in the design and construction of characters in animated films. It’s also used by cognitive scientists to identify genes, chemical compounds, and neuronal circuits that regulate the production of emotions by the brain. Such mapping could be used in the diagnosis of disorders like autism or post-traumatic stress disorder, where there is difficulty in recognizing emotions from facial expressions.
As surveillance technologies become more widespread, so do applications for sophisticated facial recognition software. These technologies appear to be ready to move from the laboratory into real life—commercialized, distributed to the masses in any number of fields, contexts, and situations. All this is happening at a time when computers are getting smarter and smarter at reading human emotion.
A team of researchers at the University of California, San Diego, founded the company Emotient, which uses machine-learning algorithms to detect emotion. The company is currently developing an app for Google Glass which will soon be on the market, according to lead scientist Marian Bartlett. The app is designed to read the emotional expressions of people who appear in the user’s field of vision in real time. Though it’s still in the testing phase, it can recognize happiness, sadness, anger, repugnance, and even scorn.
Once this kind of technology is commercially available, it’s downloadable for any Google Glass user. And beyond the recognition of specific emotions through analysis of facial movement patterns, another application of this new technology, tested by Bartlett’s team, is one that allows it to distinguish fake from true emotional expressions. In other words, it can tell if you’re lying.
The app works based on the idea that false and true expressions of emotions involve different brain mappings. While true emotional expressions are executed by the brain and the spinal cord just like a reflex, fake expressions require a conscious thought—and that involves regions of motor coordination from the cerebral cortex. As a result, the representative facial movements of true and fake emotions end up being different enough for a visual computation system to be able to detect and distinguish them. And here’s the key: Computers can make these distinctions even when humans cannot.
Upon testing, the system developed by Bartlett managed—in real time—to identify 20 of the 46 facial movements described in the FACS, according to a March report by Bartlett in Current Biology. And, even more impressive, the system not only identifies, but distinguishes authentic expressions from false expressions with an accuracy rate of 85 percent, at least in laboratory settings where the visual conditions are held constant. Humans weren’t nearly as skilled, logging an accuracy rate of about 55 percent.