# A Person Re-Identification Scheme Using Local Multiscale Feature Embedding with Dual Pyramids
## 4 Experiment and Analysis
As the results of the experiment, better performance was shown for the cases with both shaped receptive fields instead of the cases with only the square-shaped receptive fields \begin{table}
\begin{tabular}{c c} \hline \hline
**Training Setting for Backbone with** & **Rank-1** \\
**ImageNet [23] Pre-Trained Weight** & **84.51\%** \\ \hline
**ResNet50**[14] & 82.58\% \\
SeResNet101 [22] & 70.51\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Configuration evaluation result for backbone module. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{5}{c}{**Training Setting for Scale Pyramid with ResNet50 [14] Backbone**} \\ \hline \hline
**1*1** & **3*3** & **9*9** & **1*3** & **3*1** & **9*3** & **3*9** & \\ \hline O & O & X & X & X & X & X & 92.77\% \\ O & O & O & X & X & X & X & 90.88\% \\ O & O & X & O & O & X & X & 92.86\% \\
**O** & O & X & **O** & **O** & **O** & **O** & **92.99\%** \\ O & O & O & O & O & O & O & 92.36\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Configuration evaluation result for scale pyramid block. sized (1*1, 3*3) or (1*1, 3*3, 9*9). Moreover, the performance increased with the increased number of available receptive fields and showed optimal performance in the configurations with six outputs sized (1*1, 3*3, 1*3, 3*1, 3*9, 9*3). However, the performance decreased whenever a pyramid branch with a receptive field sized (9*9) was added. It is estimated that a receptive field sized (9*9) with the input feature map sized (8, 24) may be too large to extract local features properly. Through this experiment, it was shown that having shapes of receptive fields close to the visual feature elements that appear in the person image in the neural network in addition to square-shaped receptive fields commonly used in neural networks is more effective in constructing more discerning feature embedding. In the third experiment, the performance evaluation experiment was conducted on the 15 feature vectors extracted from the part pyramid based on six feature maps from the scale pyramid by changing the composition of the global pooling mechanism for local feature embedding that constitutes the output of part pyramid as suggested in (Table 3). As the results of the experiment, better performance was shown for the case with only the global average pooling used compared to only the global max pooling used or the sum of global average pooling and global max pooling used. Especially, the lowest performance was produced when the sum of the global max pooling and global average pooling was used. This can be interpreted as meaning that when these values are used together, the value becomes ambiguous, and the performance may decrease compared to when using each alone. Therefore, as shown in the previous experiments, the model composed of the scale pyramid with six outputs sized (1*1, 3*3, 1*3, 3*1, 3*9, 9*3) based on the backbone of ResNet50 [14] and the part pyramid with 15 outputs of 0, 2, 4, and 8 divisions with global average pooling can be said to have the optimal configurations for the person re-identification based on the method proposed in this study. Considering the weight constituting this model is 43 million and the weight of neural networks used as the backbone is 47 million in the model of [5], the number of auxiliary neural networks is 21 for the model of [6], and the model of [10] uses all of ResNet50 [14] as the backbone and more complex composite local feature embedding than current study, the complexity of the neural networks of the proposed method is considered to be less than or similar to previous studies. As the final experiment, the proposed model was trained by the datasets of DukeMTMC-reID [18] and Market-1501 [19], and the performance of the proposed model was evaluated and showed in (Table 4). As the results, the proposed model showed the best performance in terms of the Rank-1 accuracy of 94.79% based on the DukeMTMC-reID [18] dataset. The excellent performance