A Robot Learns the Facial Expressions Recognition and Face/Non-face Discrimination Through an Imitation Game
A Robot Learns the Facial Expressions Recognition and Face/Non-face Discrimination Through an Imitation Game
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DOI：
10.1007/s12369-014-0245-z
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发表时间：
2014-11-01
影响因子：
4.7
通讯作者：
Hafemeister, Laurence
中科院分区：
文献类型：
--
作者：
Boucenna, Sofiane;Gaussier, Philippe;Hafemeister, Laurence
在本文中，我们表明，机器人系统可以在线学习识别面部表情，而无需将面部表情与给定的抽象标签（例如，'sadness'，'happiness'）。此外，我们表明，从一个非人脸识别的脸可以自主完成，如果我们想象，学习识别一张脸发生在学习识别面部表情，而不是相反，因为它是经典的考虑。在这些实验中，机器人被认为是一个婴儿，因为我们想了解婴儿如何自主发展一些能力。我们通过机器人实验来建模、测试和分析认知能力。我们的出发点是一个数学模型，它表明，如果婴儿使用感觉运动结构来识别面部表情，那么父母必须模仿婴儿的面部表情，以实现在线学习。在这里，第一系列的机器人实验表明，一个简单的神经网络模型可以控制机器人头部，并可以在线学习，以识别人类伴侣的面部表情，如果他/她模仿机器人的原型面部表情（该系统不使用面部模型，也不使用框架系统）。第二种架构使用的节奏的互动首先允许一个强大的学习的面部表情没有面部跟踪，然后执行学习涉及的面部识别。我们更惊人的结论是，对于婴儿来说，学习识别面部可能比识别面部表情更复杂。因此，我们强调情感共鸣的重要性，作为一种机制，以确保个人之间的动态耦合，允许学习越来越复杂的任务。
In this paper, we show that a robotic system can learn online to recognize facial expressions without having a teaching signal associating a facial expression with a given abstract label (e.g., 'sadness', 'happiness'). Moreover, we show that recognizing a face from a non-face can be accomplished autonomously if we imagine that learning to recognize a face occurs after learning to recognize a facial expression, and not the opposite, as it is classically considered. In these experiments, the robot is considered as a baby because we want to understand how the baby can develop some abilities autonomously. We model, test and analyze cognitive abilities through robotic experiments. Our starting point was a mathematical model showing that, if the baby uses a sensory motor architecture for the recognition of a facial expression, then the parents must imitate the baby's facial expression to allow the online learning. Here, a first series of robotic experiments shows that a simple neural network model can control a robot head and can learn online to recognize the facial expressions of the human partner if he/she imitates the robot's prototypical facial expressions (the system is not using a model of the face nor a framing system). A second architecture using the rhythm of the interaction first allows a robust learning of the facial expressions without face tracking and next performs the learning involved in face recognition. Our more striking conclusion is that, for infants, learning to recognize a face could be more complex than recognizing a facial expression. Consequently, we emphasize the importance of the emotional resonance as a mechanism to ensure the dynamical coupling between individuals, allowing the learning of increasingly complex tasks.