• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>The Fourth joint IEEE international conferences on development and learning and epigenetic robotics >From continuous affective space to continuous expression space: Non-verbal behaviour recognition and generation
【24h】

From continuous affective space to continuous expression space: Non-verbal behaviour recognition and generation

机译：从连续的情感空间到连续的表达空间：非语言行为的识别与产生

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this research, a recurrent neural network with parametric bias (RNNPB) was adopted to construct a continuous expression space from emotion caused human behaviours. It made use of the short-term memory ability of the recurrent weights to store spatio-temporal sequences features, while the attached parametric bias units were trained in a self-organizing way and represented as a low-dimensional expression space to capture these non-linear features of the sequences. Three demonstrations were given: training and recognition performances were examined in computer simulations, while the network generated both trained and novel movements were shown in a three-dimensional avatar demonstrations.
机译：在这项研究中，采用带有参数偏差的递归神经网络（RNNPB）来构建由情绪引起的人类行为的连续表达空间。它利用循环权重的短期记忆能力来存储时空序列特征，而附加的参数偏差单元以自组织方式进行训练，并表示为低维表达空间，以捕获这些非时间序列。序列的线性特征。给出了三个演示：在计算机模拟中检查了训练和识别性能，而在三维化身演示中显示了网络生成的训练运动和新颖运动。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号