# Mmpal _sciences_
## 4 Experiments and Results
### Experiments on FERET Database  
In this section, the proposed algorithm is tested on the FERET database. The FERET dataset is a face database established in 1993 by the United States Department of Defense Advanced Research Project and FERET project team, which was established by the United States Army Research Experiment. As a result of the image having complex intra-class variability, it is often used to evaluate the performance of face recognition algorithms. It is divided into five parts: Fa (1199 subjects per image), Fb (with expression variation), Fc (image composition under different light conditions), DuPI (with age variation), and DupII (a subset of DupI). Figure 9 shows a performance comparison between the various methods and the proposed algorithm. We used face detection tools to cut and standardize the images. The recognition rate comparison results of different algorithms on the FERET database are shown in Table 7.  
Through Figure 9 and Table 5, it can be seen that, in most cases, the proposed method outperforms other methods. It is particularly evident in DupI and DupII, indicating that MMPCANet has strong robustness for samples with age variation. The average correct recognition rate in DupII is 8.9% higher than that in PCANet, and the average correct recognition rate in DupI is 8.37% higher than that in PCANet. The smallest improvement is that the proposed algorithm in Fc only increases by 2.05% compared with L1-2D2 PCANet.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Fb** & **Fc** & **DupI** & **DupII** \\ \hline ESRC [9] & 91.98 & 84.05 & 79.85 & 72.94 \\ NNAODL [10] & 96.21 & 92.78 & 88.26 & 86.22 \\ PCANet [14] & 94.44 & 91.84 & 85.33 & 86.40 \\
2 DPCANet [30] & 95.65 & 98.98 & 93.52 & 90.37 \\ L1-2D2 PCANet [19] & 97.93 & 97.82 & 93.15 & 92.48 \\ MMPCANet & **99.74** & **99.87** & **93.70** & **95.30** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average Recognition Rate (%) on FERET database.  
Figure 9: Average accuracy on FERET database.  
The above experiments show that the proposed algorithm can effectively improve the accuracy of face recognition.  
According to the above experiments, most of the traditional algorithms focus on the high-level features of the image when using features to express the image, which is an effective expression method for clean images. However, for occluded images, since occlusion is also a part of the feature, the occlusion of the input model is easily confused with facial features, and it even seriously affects the extraction of key features. It is also easy to classify occluded features as key features, which is the main problem of the low recognition rate of occluded face images. The input of each layer of MMPCANet comes from the output of all previous layers and realizes feature reuse through the parallel connection of features. This is the reason why MMPCANet can achieve good results with a low training time. The current bottleneck that keeps MMPCANet from becoming deeper is that the dimension of the resulting feature increases exponentially with the number of stages. Fortunately, the random forest classifier can solve this problem. Experiments on three datasets verify the effectiveness of the proposed method, and, compared with other methods, it can obtain better classification results.