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Affective Robots: Evaluation of Automatic Emotion Recognition Approaches on a Humanoid Robot towards Emotionally Intelligent Machines

机译:情感机器人:类人机器人对情感智能机器的自动情感识别方法评估

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One of the main aims of current social robotic research is to improve the robots’ abilities to interact with humans. In order to achieve an interaction similar to that among humans, robots should be able to communicate in an intuitive and natural way and appropriately interpret human affects during social interactions. Similarly to how humans are able to recognize emotions in other humans, machines are capable of extracting information from the various ways humans convey emotions—including facial expression, speech, gesture or text—and using this information for improved human computer interaction. This can be described as Affective Computing, an interdisciplinary field that expands into otherwise unrelated fields like psychology and cognitive science and involves the research and development of systems that can recognize and interpret human affects. To leverage these emotional capabilities by embedding them in humanoid robots is the foundation of the concept Affective Robots, which has the objective of making robots capable of sensing the user’s current mood and personality traits and adapt their behavior in the most appropriate manner based on that. In this paper, the emotion recognition capabilities of the humanoid robot Pepper are experimentally explored, based on the facial expressions for the so-called basic emotions, as well as how it performs in contrast to other state-of-the-art approaches with both expression databases compiled in academic environments and real subjects showing posed expressions as well as spontaneous emotional reactions. The experiments’ results show that the detection accuracy amongst the evaluated approaches differs substantially. The introduced experiments offer a general structure and approach for conducting such experimental evaluations. The paper further suggests that the most meaningful results are obtained by conducting experiments with real subjects expressing the emotions as spontaneous reactions.
机译:当前的社交机器人研究的主要目的之一是提高机器人与人互动的能力。为了实现类似于人与人之间的互动,机器人应该能够以直观自然的方式进行交流,并在社交互动中适当地解释人的情感。与人类如何识别其他人类的情感类似,机器能够从人类传达情感的各种方式(包括面部表情,语音,手势或文本)中提取信息,并将这些信息用于改进的人类计算机交互。可以将其描述为“情感计算”,这是一个跨学科领域,扩展到心理学和认知科学等其他无关领域,并且涉及可以识别和解释人类影响的系统的研究和开发。通过将这些情感功能嵌入类人机器人中来利用这些情感功能是“情感机器人”概念的基础,该概念的目标是使机器人能够感知用户当前的情绪和性格特征,并以此为基础以最合适的方式适应其行为。在本文中,基于所谓的基本情感的面部表情,以及与其他先进方法相比,人形机器人Pepper的情感识别能力得到了实验性探索在学术环境和真实主题中编译的表达数据库,这些数据库显示了姿势表达以及自发的情绪反应。实验结果表明,所评估方法之间的检测精度存在很大差异。引入的实验提供了进行此类实验评估的一般结构和方法。该论文进一步表明,最有意义的结果是通过对真实对象进行实验而获得的,这些对象将情绪表达为自发反应。

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