Self context-aware emotion perception on human-robot interaction
This addresses the need for robots to respond accurately in continuous interactions, though it appears incremental as it builds on existing emotion recognition methods by adding context-awareness.
The paper tackles the problem of emotion recognition in long-term human-robot interactions by introducing a self context-aware model (SCAM) that incorporates contextual information, resulting in accuracy improvements from 63.10% to 72.46% in audio, 77.03% to 80.82% in video, and 77.48% to 78.93% in multimodal modalities.
Emotion recognition plays a crucial role in various domains of human-robot interaction. In long-term interactions with humans, robots need to respond continuously and accurately, however, the mainstream emotion recognition methods mostly focus on short-term emotion recognition, disregarding the context in which emotions are perceived. Humans consider that contextual information and different contexts can lead to completely different emotional expressions. In this paper, we introduce self context-aware model (SCAM) that employs a two-dimensional emotion coordinate system for anchoring and re-labeling distinct emotions. Simultaneously, it incorporates its distinctive information retention structure and contextual loss. This approach has yielded significant improvements across audio, video, and multimodal. In the auditory modality, there has been a notable enhancement in accuracy, rising from 63.10% to 72.46%. Similarly, the visual modality has demonstrated improved accuracy, increasing from 77.03% to 80.82%. In the multimodal, accuracy has experienced an elevation from 77.48% to 78.93%. In the future, we will validate the reliability and usability of SCAM on robots through psychology experiments.