论文标题
旋转不变的步态识别与四元素卷积神经网络
Rotation-Invariant Gait Identification with Quaternion Convolutional Neural Networks
论文作者
论文摘要
基于加速步态的识别系统的理想属性是对用户在测试过程中提出的新设备方向的鲁棒性，但在培训阶段看不见。但是，这些系统中使用的传统卷积神经网络（CNN）的补偿很差。在本文中，我们通过引入QUATERNION CNN来针对此问题，Quaternion CNN是一种本质上是层次的网络体系结构，在一系列输入向量的3D旋转下是本质上的层次层次且全球不变的。 We show empirically that this network indeed significantly outperforms a traditional CNN in a multi-user rotation-invariant gait classification setting .Lastly, we demonstrate how the kernels learned by this QCNN can also be visualized as basis-independent but origin- and chirality-dependent trajectory fragments in the euclidean space, thus yielding a novel mode of feature visualization and extraction.
A desireable property of accelerometric gait-based identification systems is robustness to new device orientations presented by users during testing but unseen during the training phase. However, traditional Convolutional neural networks (CNNs) used in these systems compensate poorly for such transformations. In this paper, we target this problem by introducing Quaternion CNN, a network architecture which is intrinsically layer-wise equivariant and globally invariant under 3D rotations of an array of input vectors. We show empirically that this network indeed significantly outperforms a traditional CNN in a multi-user rotation-invariant gait classification setting .Lastly, we demonstrate how the kernels learned by this QCNN can also be visualized as basis-independent but origin- and chirality-dependent trajectory fragments in the euclidean space, thus yielding a novel mode of feature visualization and extraction.
