Real-Time recognition of extroversion-introversion trait in context of human-robot interaction

Abstract

Human personality has always been an esoteric topic in the field of social science. The five-factor model dealing personality traits plays a critical role in the field of communication studies, psychology and philosophy. Judging or recognizing personality trait is undoubtedly a cognitive aspect which requires intelligence. This judgmental process is extremely fuzzy as there are so many facets ingrained in every human. Interestingly, there is no quantitative standard to judge the severity of each facet. Intuitive and perceptive skills do the trick for the person judging personality of another person. In this work, an approach has been presented that uses only nonverbal cues to recognize extroversion-introversion personality. Major facets, e.g., human posture, facial expression, speech duration, rapid body movements, etc. are considered to recognize extroversion-introversion trait. A job interview scenario has been created in which a robot interacts with a candidate and judges his personality trait. Experimental studies validate the approach which is a promising basis for the development of computing approaches capable of predicting a specific personality trait.