Does Machines Perceive Human Emotions?

Researchers have developed a machine-learning model that takes computers a step closer to interpreting our emotions as naturally as humans do. In the growing research field of “affective computing”, robots and computers are being developed to analyze facial expressions, interpret our emotions and respond accordingly. Applications include, for instance, monitoring an individual’s health and well-being, gauging student interest in classrooms, helping diagnose signs of certain diseases, and developing helpful robot companions.

A challenge, however, is people express emotions quite differently, depending on many factors. General differences can be seen between cultures, genders, and age groups. But other differences are even more fine-grained: The time of day, how much you slept, or even your level of familiarity with a conversation partner leads to subtle variations in the way you express, say, happiness or sadness in a given moment. Human brains instinctively catch these deviations, but machines struggle. Deep-learning techniques were developed in recent years to help catch the subtleties, but they’re still not as accurate or as adaptable across different populations as they could be.

The researchers have developed a machine-learning model that outperforms traditional systems in capturing these small facial expression variations, to better gauge mood while training on thousands of images of faces. Moreover, by using a little extra training data, the model can be adapted to an entirely new group of people, with the same efficacy. The aim is to improve existing affective-computing technologies.

Human-Robotic interactions, such as for personal robotics or robots used for educational purposes, where the robots need to adapt to assess the emotional states of many different people. One version, for instance, has been used in helping robots better interpret the moods of children with autism.

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