  Gait recognition aims to distinguish different walking patterns by analyzing
video-level human silhouettes, rather than relying on appearance information.
Previous research on gait recognition has primarily focused on extracting local
or global spatial-temporal representations, while overlooking the intrinsic
periodic features of gait sequences, which, when fully utilized, can
significantly enhance performance. In this work, we propose a plug-and-play
strategy, called Temporal Periodic Alignment (TPA), which leverages the
periodic nature and fine-grained temporal dependencies of gait patterns. The
TPA strategy comprises two key components. The first component is Adaptive
Fourier-transform Position Encoding (AFPE), which adaptively converts features
and discrete-time signals into embeddings that are sensitive to periodic
walking patterns. The second component is the Temporal Aggregation Module
(TAM), which separates embeddings into trend and seasonal components, and
extracts meaningful temporal correlations to identify primary components, while
filtering out random noise. We present a simple and effective baseline method
for gait recognition, based on the TPA strategy. Extensive experiments
conducted on three popular public datasets (CASIA-B, OU-MVLP, and GREW)
demonstrate that our proposed method achieves state-of-the-art performance on
multiple benchmark tests.
