Most current gait recognition methods suffer from poor interpretability and high computational cost. To improve interpretability, we investigate gait features in the embedding space based on Koopman operator theory. The transition matrix in this space captures complex kinematic features of gait cycles, namely the Koopman operator. The diagonal elements of the operator matrix can represent the overall motion trend, providing a physically meaningful descriptor. To reduce the computational cost of our algorithm, we use a reversible autoencoder to reduce the model size and eliminate convolutional layers to compress its depth, resulting in fewer floating-point operations. Experimental results on multiple datasets show that our method reduces computational cost to 1% compared to state-of-the-art methods while achieving competitive recognition accuracy 98% on non-occlusion datasets.

翻译：当前大多数步态识别方法存在可解释性差、计算成本高的问题。为提升可解释性，本文基于库普曼算子理论研究了嵌入空间中步态特征的表征。该空间中的转移矩阵能够捕获步态周期复杂的运动学特征，即库普曼算子。算子矩阵的对角元素可表征整体运动趋势，提供具有物理意义的描述符。为降低算法计算成本，我们采用可逆自编码器缩减模型规模并消除卷积层以压缩网络深度，从而减少浮点运算次数。多数据集实验结果表明，本方法在非遮挡数据集上将计算成本降至现有最优方法的1%，同时实现98%的竞争性识别精度。