# MFCNet: Mining Features Context Network for RGB-IR Person Re-Identification
## 4 Results
### Comparison with State-of-the-Art Methods  
In this section, the proposed method is compared with other state-of-the-art methods based on the SYSU and RegDB data sets. Previous works [12; 44] provided results for most of the considered methods. The methods compared in this paper include manual feature design methods (HOG [45] and LOMO [3]) and deep learning methods [9; 15; 24; 25; 26; 27; 29; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50]. The results of the compared methods were obtained from the original text. \"-\" means that this method has not been tested.  
Table 1 shows the experimental results based on the SYSU data set. This study conducted experiments in all-search and indoor-search modes. From the superficial analysis of the results listed in the table, we can conclude that compared with manual design methods, deep learning algorithms perform better in all considered aspects. Notably, deep learning methods can learn more than manually designed features. Additionally, the indoor-search mode yields better results than the all-search mode. Our analysis suggests that the background interference in photos taken indoors is less influential in learning key features.  
In all-search mode, the experimental results of the method proposed in this paper are 51.64%, 86.80%, 93.66%, and 49.99% for r = 1, r = 10, r = 20, and the mAP, respectively. Among them, the hit rate for r = 1 is 10.28% and 9.24% higher than those of HPLIN and AlignGAN. Compared with the latest baseline [15], the proposed method yields 4.14%, 2.59%, 1.52%, and 2.34% improvements for r = 1, r = 10, r = 20 and mAP, respectively. Compared to the FBP-AL, we achieve about 1 point improvement on r = 10 and r = 20.  
In the indoor search mode, the results of this paper are 57.29%, 92.89%, 96.38%, and 64.91% for r = 1, r = 10, r = 20, and mAP, respectively. Among them, the accuracy for r = 1 is 11.52% and 11.39% higher than those of HPLIN and AlignGAN, respectively. Additionally, the proposed method performs better than the AGW method in all aspects.  
The experimental results of RegDB are shown in Table 2. The RegDB experiment in this paper is in Visible2 Infrared mode, using visible images as the query set and infrared as the gallery set. It can be seen from Table 2 that the experimental results of the method proposed in this paper are 69.76%, 86.07%, 90.73%, and 52.52% respectively. Among them, the experiments at r = 1 and r = 10 not only show the best performance but also have better performance than AlignGAN at r = 1.  
The above impressive performance suggests that our method can learn better modality-sharable features.  
Combining the experimental results of SYSU and RegDB, the method proposed in this paper can capture more discriminative features and improve the accuracy. Experiments demonstrate our method is better than the latest baseline algorithm AGW. This shows that the method proposed in this paper will provide a more powerful baseline algorithm for RGB-IR Re-ID.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Settings** & \multicolumn{4}{c}{**All-Search**} & \multicolumn{4}{c}{**Indoor Search**} \\ \hline
**Methods** & **r = 1** & **r = 10** & **r = 20** & **mAP** & **r = 1** & **r = 10** & **r = 20** & **mAP** \\ \hline HOG [45] & 2.76 & 18.3 & 32 & 4.24 & 3.22 & 24.7 & 44.6 & 7.25 \\ \hline LOMO [3] & 3.64 & 23.2 & 37.3 & 4.53 & 5.75 & 34.4 & 54.9 & 10.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the results of the proposed method and other state-of-the-art methods based on the SYSU dataset. Bold numbers are the best.