  Gait recognition is one of the most important biometric technologies and has
been applied in many fields. Recent gait recognition frameworks represent each
gait frame by descriptors extracted from either global appearances or local
regions of humans. However, the representations based on global information
often neglect the details of the gait frame, while local region based
descriptors cannot capture the relations among neighboring regions, thus
reducing their discriminativeness. In this paper, we propose a novel feature
extraction and fusion framework to achieve discriminative feature
representations for gait recognition. Towards this goal, we take advantage of
both global visual information and local region details and develop a Global
and Local Feature Extractor (GLFE). Specifically, our GLFE module is composed
of our newly designed multiple global and local convolutional layers (GLConv)
to ensemble global and local features in a principle manner. Furthermore, we
present a novel operation, namely Local Temporal Aggregation (LTA), to further
preserve the spatial information by reducing the temporal resolution to obtain
higher spatial resolution. With the help of our GLFE and LTA, our method
significantly improves the discriminativeness of our visual features, thus
improving the gait recognition performance. Extensive experiments demonstrate
that our proposed method outperforms state-of-the-art gait recognition methods
on two popular datasets.
