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Adaptation to new user interactively using dynamically calculated principal components for user-specific human-robot interaction

机译:使用动态计算的主组件进行交互方式适应新用户,以进行用户特定的人机机器人交互

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This paper presents an algorithm for interactive adaptation to new user using dynamically calculated principal components. In this algorithm, new user is adapted based on the matching score of face recognition method that use dynamically calculated eigenvectors and eigenvalues from known training face dataset. User adaptation method measures the trueness of known and unknown person using the recognition result from specific number of consecutive face images. If the value of trueness for un-known person is greater than specific threshold then the robot informs the person is unknown and asks the person name and culture information to preserve in the knowledge-base through interaction. In case of known person the system greets with that person based on his/her predefined culture. The system increments the training face dataset by including any unknown face image and subsequently recalculates the eigenvectors and eigenvalues to form new PCA. The algorithm is tested by implementing a human-robot greeting scenario with a mobile robot.
机译:本文介绍了一种使用动态计算的主组件对新用户的交互式适应算法。在该算法中,新用户基于面部识别方法的匹配得分来适应,该识别方法使用动态计算的特征向量和来自已知训练面数据集的特征向量和特征值。用户适应方法测量使用特定数量的连续面部图像的识别结果的已知和未知人的特鲁。如果未知人的真实价值大于特定的阈值,那么机器人通知该人未知,并要求人名和文化信息通过互动来保护知识库。如果是已知人,系统将根据他/她的预定文化与该人致意。系统通过包括任何未知的面部图像来递增训练面数据集,随后重新计算特征向量和特征值以形成新的PCA。通过使用移动机器人实现人机征询场景来测试该算法。

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