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和计算成本高的问题。为提升可解释性，我们基于库普曼算子理论，在嵌入空间中探究步态特征。该空间中的转换矩阵能够捕获步态周期中复杂的运动特征，即库普曼算子。算子矩阵的对角元素可表征整体运动趋势，从而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小模型规模，并去除卷积层以压缩模型深度，从而减少浮点运算次数。在多个数据集上的实验表明，与现有最优方法相比，本方法计算成本降低至1%，同时非遮挡数据集上的识别精度可达98%，具备竞争力。