# Two-Path Spatial-Temporal Feature Fusion and View Embedding for Gait Recognition
## 2 Related Work
[7] introduced the focal convolution layer (FConv), a novel convolution layer that divided the feature map into several parts to obtain part-based gait features. [PERSON] et al. [22] developed RPNet to discover the intricate interconnections between each part of gait silhouettes and then integrated them by a straightforward splicing process. GaitGL [23] and GaitStrip [24] both used 3D CNNs to construct multi-level frameworks to extract more discriminative and richer spatial features. In this paper, we design an innovative residual convolutional block (R-conv) in spatial feature extraction model by 2D CNNs, which combines regular convolution with FConv to extract both global and local gait features and enhance the discriminative capacity of the feature representations. **Temporal feature extraction modeling:** As a crucial cue for gait tasks, the current temporal feature extraction model generally employs various approaches such as 1D convolutions, LSTMs and 3D convolutions [25]. For example, [PERSON] et al. [7] and [PERSON] et al. [26] utilized 1D convolutions to model the temporal dependencies and aggregated temporal information by concatenation or in a summation. Additionally, LSTM networks were built in [21; 27] to preserve the temporal variation of gait sequences and fuse temporal information by accumulation. Moreover, some studies have proposed 3D convolutions [28; 29; 30; 31] to simultaneously extract spatial and temporal information from gait silhouette sequences. [PERSON] et al. [32] introduced MT3D network, which used 3D CNNs to extract spatial-temporal features with multiple temporal scales. However, 3D CNNs often bring complex calculations and encounter challenges during training. In this paper, we present a novel approach for temporal feature extraction modeling by 2D CNNs, which aggregates temporal information with different scales. By incorporating muti-scale temporal branches, our approach can capture rich temporal clues and empower the network to learn more discriminative motion representations adaptively. **View-invariant Modeling:** To the best of our knowledge, viewpoint change poses a formidable challenge in biometrics, particularly in face recognition and gait recognition. In contrast to face recognition, fewer methods in gait recognition have incorporated the aspect of viewpoint into their considerations. [PERSON] et al. [33] introduced a multitask generative adversarial network (GAN) and trained the GAN by using viewpoint labels as the supervision. [PERSON] et al. [34] adopted a different projection matrix as a perspective embedding method and achieved high growth on multiple backbones. However, these methods often involve a large number of parameters, making them extremely complex for effective cross-view gait recognition. Therefore, we propose a concise view model that applies view prediction learning to calculate the best view and embeds view information into our two-path spatial-temporal feature fusion module, which can significantly enhance the robustness of our network to view changes and improve gait recognition performance across varying viewpoints.