论文标题
基于Skeleon的人识别的打字样式学习
Skeleon-Based Typing Style Learning For Person Identification
论文作者
论文摘要
我们为基于打字风格的人识别提供了一种新的结构，该结构是由自适应非本地时空图形卷积网络构建的。由于类型样式动态传达了有意义的信息，这些信息对于人的身份证明很有用，因此我们提取关节位置，然后学习他们的运动动态。我们的非本地方法在分析关节位置而不是RGB数据的同时，增加了模型对嘈杂数据的鲁棒性，为交替的环境条件（例如照明，噪声等）提供了显着的鲁棒性。我们进一步介绍了两个新的数据集，用于键入基于样式的人识别任务，并显示了模型的较高歧视和常规级别的典范，并在整体上进行了总体化的范围。
We present a novel architecture for person identification based on typing-style, constructed of adaptive non-local spatio-temporal graph convolutional network. Since type style dynamics convey meaningful information that can be useful for person identification, we extract the joints positions and then learn their movements' dynamics. Our non-local approach increases our model's robustness to noisy input data while analyzing joints locations instead of RGB data provides remarkable robustness to alternating environmental conditions, e.g., lighting, noise, etc. We further present two new datasets for typing style based person identification task and extensive evaluation that displays our model's superior discriminative and generalization abilities, when compared with state-of-the-art skeleton-based models.
