# Gait Energy Response Functions for Gait Recognition against Various Clothing and Carrying Status
## 4 Experiments
### Comparison on Clothing and Carrying Status Variations
#### 4.5.1 OU-TD-B and OU-LP-Bag \(\beta\)  
The state-of-the-art methods for comparison contain SVB-frieze pattern [24], Component-based [44], Whole-based [16], Part-based [28], Part-EnDFT [29], AESI + ZNK [45], GEI+RSM [26], Gabor+RSM-HDF [27], Gabor GEI [18], two-point gait (TPG) + GEI [46], GEI w/LDA [47], GEI w/2 DLDA [48], GEI w/CSA [49], GEI w/DATER [25], GEI w/Ranking SVM [50] and JIS-ML [22]. Moreover, we evaluated a CNN-based method, i.e., GEINet [34] as one of the benchmarks to make a comparison with deep learning-based methods. We trained the GEINet using the same dataset protocol as our GERF model. All default hyper-parameters are chosen except for the number of units on the fully connected layer fc4, which is equal to the number of subjects in the training set. Thus, we changed it according to our datasets. Finally, the DET curves with z-normalization and CMC curves of all the methods are shown in Figure 7 and EERs with z-normalization and rank-1 identification rates are shown in Table 2.  
The proposed Gabor+SD-GERF method achieves the best or second-best performance for both datasets, which shows that this method is effective and robust for gait recognition under variations in clothing and carrying status. Although Gabor+RSM-HDF [27] got the best rank-1 identification rate in OU-TD-B, we must point out that the RSM framework suffers from three weaknesses: (1) It can be only applied to identification scenarios (not to verification scenarios) because it relies on majority voting in all the galleries; (2) Because it requires multiple samples per gallery to compute a within-class scatter from the gallery set, it cannot be applied to datasets with a single sample per gallery (e.g., OU-LP-Bag \(\beta\)); and (3) It cannot guarantee reproducibility because it contains a random selection process at the metric learning stage. Moreover, compared with the joint intensity transformation-based method (JIS-ML [22]), the proposed method achieves higher performance in both datasets except for a slightly lower z-EER (0.15%) in OU-LP Bag \(\beta\). \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**OU-TD-B**} & \multicolumn{2}{c}{**OU-LP-Bag \(\beta\)**} \\ \hline
**Methods** & **z-EER** & **Rank-1** & **z-EER** & **Rank-1** \\ \hline SVB-frieze pattern [24] & 19.81 & - & - & - \\ Component-based [44] & 18.25 & - & - & - \\ Whole-based [16] & 14.88 & 58.1 & - & - \\ Part-based [28] & 10.26 & 66.3 & - & - \\ Part-EnDFT [29] & - & 72.8 & - & - \\ AESI+ZNK [45] & - & 72.7 & - & - \\ GEI+RSM [26] & N/A & 80.4 & N/A & N/A \\ Gabor+RSM-HDF [27] & N/A & **90.7** & N/A & N/A \\ Gabor GEI [18] & 11.80 & 62.3 & 10.48 & 46.4 \\ TPG+GEI [46] & 7.10 & - & - & - \\ GEI w/LDA [47] & 15.63 & 54.3 & 8.10 & 54.6 \\ GEI w/2 DLDA [48] & 8.91 & 70.7 & 11.47 & 43.3 \\ GEI w/CSA [49] & 16.00 & - & - & - \\ GEI w/DATER [25] & 8.72 & - & - & - \\ GEI w/Ranking SVM [50] & 10.75 & 58.4 & 10.81 & 28.3 \\ JIS-ML [22] & 6.66 & 74.5 & **5.45** & 57.4 \\ GEINet [34] & 8.38 & 60.2 & 9.75 & 40.7 \\ Gabor+Global GERF [20] & **5.14** & 82.7 & 6.67 & 58.3 \\ Gabor+SD-GERF (proposed) & **4.61** & **87.4** & **5.60** & **64.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: EER with z-normalization [%] and rank-1 identification rates [%] compared with the state-of-the-art methods. N/A and “-” mean not applicable and not provided, respectively. Even compared with GEINet, the proposed method achieves better performance. This is because that the GEINet was firstly designed for cross-view gait recognition on the OU-ISIR large population dataset (OULP) [51] which uses more than 15,000 samples. When compared with this large population dataset, the clothing and carrying status datasets we used in our experiments (about 1300 samples for OU-TD-B and 4000 samples for OU-LP-Bag \(\beta\)) are quite small although they have the largest clothing and carrying status variations among publicly available gait datasets. Such small datasets cause the overfitting problem and hence the GEINet suffers from large generalization errors.