Self context-aware emotion perception on human-robot interaction

• URL: http://arxiv.org/abs/2401.10946v1
• Date: Thu, 18 Jan 2024 10:58:27 GMT
• Title: Self context-aware emotion perception on human-robot interaction
• Authors: Zihan Lin, Francisco Cruz, and Eduardo Benitez Sandoval
• Abstract summary: Humans consider that contextual information and different contexts can lead to completely different emotional expressions. We introduce self context-aware model (SCAM) that employs a two-dimensional emotion coordinate system for anchoring and re-labeling distinct emotions. This approach has yielded significant improvements across audio, video, and multimodal environments.
• Score: 3.775456992482295
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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