  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.
