Automatic, Dimensional and Continuous Emotion Recognition

TLDR

Recognition and analysis of human emotions have attracted extensive research across neuroscience, psychology, cognitive sciences, and computer sciences, with a recent shift toward continuous, context‑specific recognition in naturalistic settings and multimodal integration. This article explores recent advances in dimensional and continuous affect modeling, sensing, and automatic recognition. It focuses on visual, audio, tactile, and brain‑wave modalities.

Abstract

Recognition and analysis of human emotions have attracted a lot of interest in the past two decades and have been researched extensively in neuroscience, psychology, cognitive sciences, and computer sciences. Most of the past research in machine analysis of human emotion has focused on recognition of prototypic expressions of six basic emotions based on data that has been posed on demand and acquired in laboratory settings. More recently, there has been a shift toward recognition of affective displays recorded in naturalistic settings as driven by real world applications. This shift in affective computing research is aimed toward subtle, continuous, and context-specific interpretations of affective displays recorded in real-world settings and toward combining multiple modalities for analysis and recognition of human emotion. Accordingly, this article explores recent advances in dimensional and continuous affect modeling, sensing, and automatic recognition from visual, audio, tactile, and brain-wave modalities.