# A Lightweight Efficient Person Re-Identification Method Based on Multi-Attribute Feature Generation
## 4 Experiments and Results
### Discussion  
As mentioned above, this study developed a lightweight person multi-attribute feature-generation framework for person re-ID. While maintaining the accuracy, the parameters of the proposed framework are less than for ResNet for the global person feature representation. Generally, the person re-ID algorithms are commonly used to obtain the person recognition accuracy, which is evaluated by the cumulative match characteristic (CMC) curve in real scene applications. The value of CMC@k indicates the percentage of real recognition ranked in the top \(k\). For each inquired person image \(p_{i}\in P\), all gallery images are ranked based on the feature similarity, which recognizes by using the person re-ID algorithms. In this case, the efficiency of feature generation is very important for real pedestrian surveillance and recognition. The time complexity for optimizing the proposed framework is as low as \(O(n)\) for feature generation, which is quite suitable for applications in many mobile terminal devices that have limited computing resources [56]. The MagicBook Pro series smart Tablet PC is an example of this type of device; it is made by HuaWei Tech. Co. and can match more than 5000 special instances in 3 s. This device adapts to the high-speed computing technology of video processing and analyzing for retrieving person instances.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**DataSet Attribute Net**} & \multicolumn{3}{c}{**CUHK-01**} & \multicolumn{6}{c}{**CUHK-03**} & \multicolumn{6}{c}{**VIPeR**} & \multicolumn{2}{c}{**Market-1501**} \\ \cline{2-13}  & **R1** & **R5** & **R20** & **R1** & **R5** & **R20** & **R1** & **R5** & **R20** & **R1** & **R5** & **R20** & **R1** & **mAP** \\ \hline a-Net & 79.21 & 90.32 & 97.12 & 75.02 & 86.34 & 95.21 & 80.13 & 90.12 & 95.34 & 60.12 & 77.43 & 93.13 & 80.23 & 73.23 \\ \hline b-Net & 80.13 & 92.32 & 98.15 & 77.31 & 87.12 & 96.34 & 81.31 & 91.23 & 96.12 & 62.31 & 79.12 & 94.12 & 82.32 & 74.13 \\ \hline c-Net & 80.02 & 92.09 & 98.03 & 76.23 & 88.43 & 94.12 & 80.57 & 91.54 & 96.32 & 63.13 & 77.47 & 92.34 & 83.12 & 75.21 \\ \hline Multi-Attribute Net & **83.92** & **92.82** & **99.89** & **78.50** & **92.10** & **98.09** & **82.50** & **92.15** & **99.03** & **64.97** & **80.08** & **98.74** & **93.84** & **78.76** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performance of the single sub-Net.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attribute Factors** & **Parameters** & **Rank-1** & **Rank-5** & **Rank-20** \\ \hline \(\lambda_{a}+\lambda_{b}+\lambda_{c}\) & 0.2 + 0.4 + 0.4 & 60.34 & 76.52 & 93.46 \\  & 0.3 + 0.4 + 0.3 & 62.25 & 78.52 & 91.35 \\  & 0.4 + 0.4 + 0.2 & 64.03 & 79.02 & 96.37 \\  & 0.5 + 0.4 + 0.1 & **64.97** & **80.08** & **98.74** \\ \hline \hline \end{tabular}
\end{table}
Table 7: The attribute factors learning on VIPeR dataset.  
Therefore, the proposed algorithm equipped with fewer parameters has the advantage application for real person re-ID technology.  
In addition, the parameters in our framework are all shared for unified training. The holistic network structure of CaffeNet is almost the simplest comparing with the common deep neural networks except the LeNet [57]. However, the LeNet can not achieve the performance that the CaffeNet does. So, we think our proposed framework is a lightweight algorithm that can be deployed on the mobile devices. The comparative results are shown in Table 8 (\"ins\" denotes \"instances\"). It can be seen that the proposed method is \"lightweight\" with the CaffeNet as a backbone network when weighing various factors (Params, accuracy, and time).