# A Lightweight Efficient Person Re-Identification Method Based on Multi-Attribute Feature Generation
## 4 Experiments and Results
### Ablation Study  
As mentioned above, the proposed method provides the best rank@1 accuracy. We would like to visualize the middle-layer descriptors for each attribute network and show the low-level visual descriptors (e.g., color and body information). Furthermore, we prove the performance of the single-attribute network.  
Figure 8 shows the visualized descriptor, which means the low-level visual information (e.g., color and region information) of a person can be retained in the first two convolutional layers (shallow layer network structure). The first two convolutional layers show that the person representation (color and body information) becomes vaguer upon increasing the number of hidden layers. In fact, the color descriptor is quite obvious in the first convolutional layer feature maps and then fades away beyond a few layers for the deep network [32]. In addition, the region information for each person is also visible in Figure 8.  
Figure 8: The feature maps of the first and second convolutional layers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Rank = 1** & **Rank = 5** & **Rank = 10** & **Rank = 20** \\ \hline FPNN [23] & 20.65 & - & 61.50 & 80.00 \\ MLF [27] & 29.11 & 52.34 & 65.95 & 79.87 \\ kLFDA [28] & 32.30 & 65.80 & 79.70 & 90.90 \\ IDLA [29] & 34.81 & - & 76.00 & - \\ TJAIDL [52] & 38.50 & - & - & - \\ LOMO+XQDA [34] & 40.00 & 68.13 & 80.51 & 91.08 \\ DeepFeature [53] & 40.50 & - & 70.4 & 89.26 \\ DeepList [54] & 40.51 & 69.15 & 81.04 & 91.07 \\ MuDeep [5] & 43.03 & 74.36 & 85.76 & - \\ PAUL [4] & 45.20 & - & - & - \\ MVLDML+ [31] & 50.03 & 79.21 & 88.54 & 94.70 \\ Switchable+ [55] & 51.60 & 72.90 & 80.80 & - \\ MSFANet [8] & 52.2 - & - & - & \\ DDAN [6] & 52.30 & 60.60 & 71.80 & - \\ MetaBIN [7] & 59.90 & 78.40 & 82.80 & - \\ \hline Ours & 64.97 & 80.08 & 92.02 & 98.74 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results on VIPeR.  
In addition, we also visualized the weights (or the coefficients of kernel filters) in Figure 6, which shows 96 kernels for the first convolutional layer and 384 convolutional kernels for the fourth layer. Each square in Figure 6 is a hidden unit. Different hidden units represent different edge positions and orientations in the image. Generally, the sharper the edge is, the more discriminative it is of the descriptor.  
This study has also given the performance for each single attribute network in Table 6, which shows the recognition result for each descriptor. From Table 6, we can see that the color attribute network (c-Net) and the body attribute network (b-Net) have a better performance comparing with the accessory attribute networks. The multi-attribute networks could construct a global person feature inspired by the visual processing mechanism of human beings, which serves as a key to depict the person representation.  
Furthermore, we conducted the ablation study on the VIPeR dataset for the attribute weight factors. The results are shown in Table 7. Generally, we think the accessory attribute would bear the more robust information comparing with the color attribute that is more fragile as the environment changed. In this ablation study, we continued to increase the weight of the accessory attribute.