# Gait Energy Response Functions for Gait Recognition against Various Clothing and Carrying Status
## 4 Experiments
### Discussion  
#### 4.6.1 Comparison on Speed Variation  
We discussed the performance of proposed method on speed variation, which involves different covariate conditions compared with clothing and carrying status. While the clothing and carrying status variations tend to affect the static components (e.g., torso and limb shapes) more than the dynamic ones (e.g., changes in stride and arm swing), speed variation displays the opposite pattern. The OU-ISIR Gait Database, Treadmill Dataset A [21] (OU-TD-A) is used for this experiment, because it has the largest speed variation among publicly available gait databases. We trained a common GERF model for all speed cases. Table 4 shows the rank-1 identification rates. From the results, the proposed method achieves the second-best performance. However, it is noted that SSGEI [55] which achieves the best performance is specifically designed for solving the speed variation.  
We also made some qualitative evaluation in Figure 8, which shows typical feature examples and profile of Global GERF in OU-TD-A. We find that the obtained Global GERF (bottom row of Figure 8a) is more insensitive to the difference between dynamic components of the legs and arms and  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Methods** & **Set-A** & **Set-B** & **Set-C** & **Average** \\ \hline GEI [11] & **99** & 60 & 30 & 63.0 \\ GEIN [12] & 98.3 & 80.1 & 33.5 & 70.6 \\ STIP+NN [54] & 95.4 & 60.9 & 52 & 69.4 \\ AESI+ZNK [45] & **100** & **93.1** & 81.3 & 91.5 \\ L-CRF [52] & 98.6 & 90.2 & 85.8 & 91.5 \\ GEINet [34] & 97.5 & 84.5 & 71.8 & 84.6 \\ DCNN [53] & 95.6 & 88.3 & 76.2 & 86.7 \\ Gabor+Global GERF [20] & **99** & 91 & **92** & **94.0** \\ Gabor+SD-GERF (proposed) & **99** & **100** & **96** & **98.3** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Side-view rank-1 identification rates [%] compared with other state-of-the-arts on three probe sets under different walking conditions of CASIA-B dataset.  
the background, than the original GEI (top row of Figure 8a). This is also understandable from the profile shown in Figure 8b. Intensity differences between background (intensity values around 0) and typical dynamic components (intensity values around 127) are compressed (i.e., nearly flat), and hence the dynamic components approach background intensity. In contrast, intensity differences between the typical dynamic components and foreground (intensity values around 255) are emphasized. This profile suggests a benefit of discriminating foreground from the others, regardless of whether they are background or dynamic components. Hence, the proposed GERF can attenuate the dynamic components, which are greatly affected by speed variation, and simultaneously highlight the differences in static components.  
Through this discussion of speed variation, we confirm the flexibility of the proposed GERF framework, in which the response functions are learned in a data-driven way, and show the potential for wide application of robust gait recognition under various covariate conditions, including different clothing, carrying status, and speed.