The Implementation and Evaluation of Individual Preference in Robot Facial Expression Based on Emotion Estimation Using Biological Signals

Abstract

Recently, robot services have been widely applied in many fields. To provide optimum service, it is essential to maintain good acceptance of the robot for more effective interaction with users. Previously, we attempted to implement facial expressions by synchronizing an estimated human emotion on the face of a robot. The results revealed that the robot could present different perceptions according to individual preferences. In this study, we considered individual differences to improve the acceptance of the robot by changing the robot’s expression according to the emotion of its interacting partner. The emotion was estimated using biological signals, and the robot changed its expression according to three conditions: synchronized with the estimated emotion, inversely synchronized, and a funny expression. During the experiment, the participants provided feedback regarding the robot’s expression by choosing whether they “like” or “dislike” the expression. We investigated individual differences in the acceptance of the robot expression using the Semantic Differential scale method. In addition, logistic regression was used to create a classification model by considering individual differences based on the biological data and feedback from each participant. We found that the robot expression based on inverse synchronization when the participants felt a negative emotion could result in impression differences among individuals. Then, the robot’s expression was determined based on the classification model, and the Semantic Differential scale on the impression of the robot was compared with the three conditions. Overall, we found that the participants were most accepting when the robot expression was calculated using the proposed personalized method.

Multi-Net Fusion: Exploring a Brain-Inspired Neural Network Model for Facial Expression Recognition

“…However, recent psychological and neurological studies have modeled the human expression state as a two-dimensional space comprising valence and arousal dimensions [33], indicating that the emotions represented by each expression feature can be continuous, and there are no fixed boundaries between emotions. This model has been used for reference in the field of robotic intelligence [5].…”

“…Usually, facial expressions produce 55% of the effects during social interaction [1]. Facial Expression Recognition (FER) related research has attracted widespread attention in recent years, especially in the fields of healthcare [2], mental illness prevention [3], customer interest analysis [4], intelligent service robots [5], etc. Moreover, FER plays a good supplementary role in the robustness research of face recognition [6] [7].…”

Explaining Physiological Affect Recognition With Optimized Ensembles of Clustered Explainable Models

“…Then, an algorithm translates the measurements into affective information such as discrete emotion labels. Real-time affective information can be used for sophisticated human-robot interactions [5] [6] or gauging user preferences for targeted advertisements [7] [8].…”