# Gait Recognition Based on Gait Optical Flow Network with Inherent Feature Pyramid
## 5 Conclusions  
In this paper, since many methods concern the long-term motion of gait but ignore the short-term motion of gait, a novel GOFI gait representation is proposed to extract the instantaneous motion as well as the silhouette's edge. Then, the gait features are extracted \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Representation**} & \multicolumn{4}{c}{**Permutation Invariant Function**} & \multicolumn{4}{c}{**Result**} \\ \hline
**GEI** & **GOFI** & **ST** & **Max** & **Mean** & **Median** & **Attention** & **IM** & **BG** & **CL** \\ \hline \(\surd\) & & & & & & & 76.4 & 64.1 & 31.8 \\ \(\surd\) & & \(\surd\) & & & & \(\surd\) & 93.6 & 85.0 & 62.2 \\  & \(\surd\) & \(\surd\) & & & & \(\surd\) & 96.4 & 85.5 & 66.1 \\  & \(\surd\) & \(\surd\) & & & & & 94.1 & 82.3 & 64.7 \\  & \(\surd\) & & \(\surd\) & & & \(\surd\) & 92.1 & 83.8 & 62.0 \\  & \(\surd\) & & & \(\surd\) & & \(\surd\) & 86.5 & 75.2 & 48.3 \\  & \(\surd\) & & & & \(\surd\) & & 86.1 & 74.8 & 41.1 \\  & \(\surd\) & & & & \(\surd\) & & 91.8 & 83.3 & 63.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiments for each structure of GOFN conducted on CASIA-B. Specifically, GEI and GOFI represent different inputs for the network. ST, Max, Mean, Median, and Attention represent different permutation invariant functions to extract set-level features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Training Set** & **Model** & **FW** & **SW** & **BW** & **Average** \\ \hline \multirow{2}{*}{24} & LGSD + PSN [35] & 58.6 & 56.0 & 38.1 & 50.9 \\  & GOFN & 64.2 & 54.3 & 39.5 & 52.7 \\ \hline \multirow{2}{*}{62} & LGSD + PSN [35] & 63.6 & 60.0 & 42.3 & 55.3 \\  & GOFN & 69.3 & 58.2 & 43.5 & 57.0 \\ \hline \multirow{2}{*}{100} & LGSD + PSN [35] & 71.7 & 71.0 & 50.5 & 64.4 \\  & GOFN & 76.1 & 70.5 & 51.2 & 67.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recognition accuracy (%) under different conditions on CASIA-C with different training sets. Compared with the PSN model, GOFN has better performance under FW and BW conditions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Loss Function** & **NM** & **BG** & **CL** \\ \hline Softmax & 31.9 & 27.9 & 11.8 \\ Triplet loss & 94.2 & 83.1 & 62.5 \\ Combined loss & 96.4 & 85.5 & 66.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation experiments for different loss functions on CASIA-B. The combined loss function performs better than any function alone. by the GOFN, which contains an ST architecture to aggregate the image-level features to the set-level features and an IFP to exploit the multi-scaled partial features. The experiments and comparisons conducted on the CASIA-B and the CASIA-C gait datasets show that the GOFN achieves better performances than previous methods under both the cross-view condition and the identical-view condition, which proves the advantages of the GOFN. Moreover, the ablation experiments prove that the GOFI is more effective than the GEI and that the ST and IFP components are also have important impacts on the accuracy of gait recognition. In the future, it will be a very interesting research area to explore the use of walking habits for recognition, since walking habits can be used to theoretically explain the connection between identity and gait. Conceptualization, H.Y. and K.X. ; Methodology, H.Y. ; Validation, K.X. ; Formal analysis, H.Y. ; Investigation, H.Y. ; Resources, T.S. ; Data curation, K.X. ; Writing--original draft, H.Y. ; Writing--review & editing, T.S. and K.X. ; Supervision, T.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Nature Science Foundation of China (grant number: 62372295, 62002220). Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Publicly available datasets were analyzed in this study.