Could a program detect potential terrorists by reading their facial expressions and behavior? This was the hypothesis put to the test by the US Transportation Security Administration (TSA) in 2003, as it began testing a new surveillance program called the Screening of Passengers by Observation Techniques program, or Spot for short Emotion detection technology requires two techniques: computer vision, to precisely identify facial expressions, and machine learning algorithms to analyze and interpret the emotional content of those facial features. Typically, the second step employs a technique called supervised learning, a process by which an algorithm is trained to recognize things it has seen before. The basic idea is that if you show the algorithm thousands and thousands of images of happy faces with the label “happy” when it sees a new picture of a happy face, it will, again, identify it as “happy”. Amazon, Microsoft and IBM now advertise “emotion analysis” as one of their facial recognition products, and a number of smaller firms, such as Kairos and Eyeris, have cropped up, offering similar services to Affectiva. Beyond market research, emotion detection technology is now being used to monitor and detect driver impairment, test user experience for video games and to help medical professionals assess the well being of patients.