Deformation of gait silhouettes caused by different view angles heavily affects the performance of gait recognition. In this paper, a new method based on deterministic learning and knowledge fusion is proposed to eliminate the effect of view angle for efficient view-invariant gait recognition. First, the binarized walking silhouettes are characterized with three kinds of time-varying width parameters. The nonlinear dynamics underlying different individuals’ width parameters is effectively approximated by radial basis function (RBF) neural networks through deterministic learning algorithm. The extracted gait dynamics captures the spatio-temporal characteristics of human walking, represents the dynamics of gait motion, and is shown to be insensitive to the variance across various view angles. The learned knowledge of gait dynamics is stored in constant RBF networks and used as the gait pattern. Second, in order to handle the problem of view change no matter the variation is small or large, the learned knowledge of gait dynamics from different views is fused by constructing a deep convolutional and recurrent neural network (CRNN) model for later human identification task. This knowledge fusion strategy can take advantage of the encoded local characteristics extracted from the CNN and the long-term dependencies captured by the RNN. Experimental results show that promising recognition accuracy can be achieved.

由不同视角引起的步态轮廓变形严重影响步态识别的性能。本文提出了一种基于确定性学习和知识融合的新方法，以消除视角对有效的视角不变步态识别的影响。首先，二值化步行轮廓具有三种随时间变化的宽度参数。径向基函数（RBF）神经网络通过确定性学习算法有效地逼近了不同个体的宽度参数所基于的非线性动力学。提取的步态动力学捕获了人类步行的时空特征，代表了步态运动的动力学，并且显示出对各种视角的变化都不敏感。所学的步态动力学知识存储在恒定的RBF网络中，并用作步态模式。其次，为了处理无论大小变化的视图变化问题，通过构建深度卷积和递归神经网络（CRNN）模型以供以后的人类识别任务，融合了从不同视图学到的步态动力学知识。这种知识融合策略可以利用从CNN中提取的编码局部特征以及RNN捕获的长期依存关系。实验结果表明，可以实现有希望的识别精度。通过构建深度卷积和递归神经网络（CRNN）模型以用于以后的人类识别任务，融合了从不同角度学到的步态动力学知识。这种知识融合策略可以利用从CNN中提取的编码局部特征以及RNN捕获的长期依存关系。实验结果表明，可以实现有希望的识别精度。通过构建深度卷积和递归神经网络（CRNN）模型以用于以后的人类识别任务，融合了从不同角度学到的步态动力学知识。这种知识融合策略可以利用从CNN中提取的编码局部特征以及RNN捕获的长期依存关系。实验结果表明，可以实现有希望的识别精度。