scholarly journals The study of attention estimation for child-robot interaction scenarios

2020 ◽  
Vol 9 (3) ◽  
pp. 1220-1228
Author(s):  
Muhammad Attamimi ◽  
Takashi Omori
Keyword(s):  
Support Vector Machine ◽  
Facial Expression ◽  
Mental States ◽  
Free Play ◽  
Support Vector ◽  
Robot Interaction ◽  
Multimodal Signals ◽  
Human Agent ◽  
Agent Interaction ◽  
Attention Level

One of the biggest challenges in human-agent interaction (HAI) is the development of an agent such as a robot that can understand its partner (a human) and interact naturally. To realize this, a system (agent) should be able to observe a human well and estimate his/her mental state. Towards this goal, in this paper, we present a method of estimating a child's attention, one of the more important human mental states, in a free-play scenario of child-robot interaction (CRI). To realize attention estimation in such CRI scenario, first, we developed a system that could sense a child's verbal and non-verbal multimodal signals such as gaze, facial expression, proximity, and so on. Then, the observed information was used to train a model that is based on a Support Vector Machine (SVM) to estimate a human's attention level. We investigated the accuracy of the proposed method by comparing with a human judge's estimation, and obtained some promising results which we discuss here.

Intersubjectivity in human–agent interaction

2007 ◽  
Vol 8 (3) ◽  
pp. 391-410 ◽  
Author(s):  
Justine Cassell ◽  
Andrea Tartaro
Keyword(s):  
Human Robot Interaction ◽  
Embodied Conversational Agents ◽  
Conversational Agents ◽  
Robot Interaction ◽  
Human Agent ◽  
Unconscious Communication ◽  
Agent Interaction ◽  
And Behavior ◽  
And Robotics

What is the hallmark of success in human–agent interaction? In animation and robotics, many have concentrated on the looks of the agent — whether the appearance is realistic or lifelike. We present an alternative benchmark that lies in the dyad and not the agent alone: Does the agent’s behavior evoke intersubjectivity from the user? That is, in both conscious and unconscious communication, do users react to behaviorally realistic agents in the same way they react to other humans? Do users appear to attribute similar thoughts and actions? We discuss why we distinguish between appearance and behavior, why we use the benchmark of intersubjectivity, our methodology for applying this benchmark to embodied conversational agents (ECAs), and why we believe this benchmark should be applied to human–robot interaction.

Combining 2D Gabor and Local Binary Pattern for Facial Expression Recognition Using Extreme Learning Machine

2019 ◽  
Vol 23 (3) ◽  
pp. 444-455 ◽  
Author(s):  
Zhen-Tao Liu ◽  
Si-Han Li ◽  
Wei-Hua Cao ◽  
Dan-Yun Li ◽  
Man Hao ◽  
...  
Keyword(s):  
Facial Expression ◽  
Extreme Learning Machine ◽  
Facial Expression Recognition ◽  
Local Binary Pattern ◽  
Human Robot Interaction ◽  
Support Vector ◽  
Expression Recognition ◽  
Robot Interaction ◽  
Interaction Detection ◽  
Learning Machine

The efficiency of facial expression recognition (FER) is important for human-robot interaction. Detection of the facial region, extraction of discriminative facial expression features, and identification of categories of facial expressions are all related to the recognition accuracy and time-efficiency. An FER framework is proposed, in which 2D Gabor and local binary pattern (LBP) are combined to extract discriminative features of salient facial expression patches, and extreme learning machine (ELM) is adopted to identify facial expression categories. The combination of 2D Gabor and LBP can not only describe multiscale and multidirectional textural features, but also capture small local details. The FER of ELM and support vector machine (SVM) is performed using the Japanese female facial expression database and extended Cohn-Kanade database, respectively, in which both ELM and SVM achieve an accuracy of more than 85%, and the computational efficiency of ELM is higher than that of SVM. The proposed framework has been used in the multimodal emotional communication based humans-robots interaction system, in which FER within 2 seconds enables real-time human-robot interaction.

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