# Gait Recognition Based on Gait Optical Flow Network with Inherent Feature Pyramid
## 4 Experiments
### Comparisons with Other Methods  
The performance of the GOFN is compared with other methods on CASIA-B and CASIA-C. As shown in Table 1, the experiments show the effectiveness of the GOFN on CASIA-B under cross-view conditions. Our approach achieves an average rank-1 accuracy of 96.4% under normal walking conditions (NM), an accuracy of 85.5% under bag-carrying (BG), and an accuracy of 66.1% under coat-wearing walking (CL) conditions on CASIA-B, excluding identical-view cases. It shows that the accuracy of the GOFN is highest at 144\({}^{\circ}\), and this may be because the spatial and motion features are most abundant in this view. It also indicates that the accuracy under the BG condition is lower than under the NM condition, and the accuracy is the lowest under the CL condition. This may be because the appearances are changed under the BG condition while motion information remains the same, and both spatial and motion features are partly concealed under the CL condition, which brings significant challenges to gait recognition. Compared with the SPAE method proposed by [31] and the MGAN proposed by [32], the GOFN is much more accurate. Compared with Gaitset [9], the accuracy is 4.4% higher on average under NM conditions. Gaitset uses only the silhouette while the GOFI uses both the instantaneous motion information as well as the silhouette edge. The edge of the silhouette in the GOFI retains useful spatial information and is beneficial for accuracy under NM conditions. Moreover, the accuracy is 3.6% higher on average under CL conditions. This may be because the GOFI leverages the motion information, which excludes the influence of the outline of silhouette, and the Set Transition works well. Compared with Gait-D [33] and GaitGraph [21], the methods based on skeleton features, the GOFN performs better under NM and BG conditions, although Gait-D obtains a better result under CL conditions. This may be because skeleton features are free from the errors caused by the coat. Compared with methods using local patterns such as GaitPart [14] and GaitGL [15], the GOFN achieves better results in NM conditions but still encounters issues in BG and CL conditions. However, GaitPart [14] and GaitGL [15] split the original sequence into partitions. In the datasets with aligned subject heights, the methods perform well. But in situations where the heights of subjects change heavily, the methods may encode the misplacement of partitions. However, in our method, the features are extracted from the global silhouettes and the global instantaneous motion, which is more robust when applying the model to subjects with different heights. As shown in Table 2, the experiments show the effectiveness of the GOFN on CASIA-B under the identical-view condition. The performance of the GOFN is compared with other approaches on CASIA-B under identical-view conditions. Under the identical view, the GOFN performs better than other methods. It achieves an accuracy of 98.2% under the NM conditions, 87.5% under the BG conditions, and 69.4% under the CL conditions. Compared with the SPAE method proposed by [28], PoseGait proposed by [34], and LGSD + PSN proposed by [35], the GOFN obtains better performance because the instantaneous motion is less affected under identical-view conditions. Moreover, the GOFN shows its superiority  
Figure 5: The examples from CASIA-B (**a**) and CASIA-C (**b**). (**a**) From left to right, the figures show the CL condition, the BG condition, and the NM condition in different views. (**b**) From left to right, the figures show the BW condition, the SW condition, and the FW condition. under different conditions, and it exceeds the previous method by at least 2.5% under BG conditions and by at least 5.2% under CL conditions. This shows that the GOFI captured more instantaneous motion using the optical flow, which improves the performance of gait representation.