Do Smart Glasses Dream of Sentimental Visions?

Emotion recognition in smart eyewear devices is valuable but challenging. One key limitation of previous works is that the expression-related information like facial or eye images is considered as the only evidence of emotion. However, emotional status is not isolated; it is tightly associated with people's visual perceptions, especially those with emotional implications. However, little work has examined such associations to better illustrate the causes of emotions. In this paper, we study the emotionship analysis problem in eyewear systems, an ambitious task that requires classifying the user's emotions and semantically understanding their potential causes. To this end, we describe EMOShip, a deep-learning-based eyewear system that can automatically detect the wearer's emotional status and simultaneously analyze its associations with semantic-level visual perception. Experimental studies with 20 participants demonstrate that, thanks to its awareness of emotionship, EMOShip achieves superior emotion recognition accuracy compared to existing methods (80.2% vs. 69.4%) and provides a valuable understanding of the causes of emotions. Further pilot studies with 20 additional participants further motivate the potential use of EMOShip to empower emotion-aware applications, such as emotionship self-reflection and emotionship life-logging.