首页> 外文会议>2010 IEEE/SICE International Symposium on System Integration >Adaptation to new user interactively using dynamically calculated principal components for user-specific human-robot interaction
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Adaptation to new user interactively using dynamically calculated principal components for user-specific human-robot interaction

机译:使用动态计算的主要组件以交互方式适应新用户,以实现特定于用户的人机交互

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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:本文提出了一种使用动态计算的主成分进行交互式适应新用户的算法。在该算法中,基于人脸识别方法的匹配分数来适应新用户,该方法使用动态计算的特征向量和来自已知训练人脸数据集的特征值来进行匹配。用户适应方法使用来自特定数量的连续面部图像的识别结果来测量已知和未知人员的真实性。如果未知人员的真实性值大于特定阈值,则机器人会通知该人员未知,并通过交互作用要求该人员的姓名和文化信息保存在知识库中。在已知人员的情况下,系统根据他/她的预定义文化与该人员打招呼。系统通过包括任何未知的面部图像来增加训练面部数据集,然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交互问候场景来测试该算法。

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