# Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition
## 5 Experimental Results
### Experiment on LFWA Dataset  
The second personal face database chosen is the largest database--the aligned labeled face in the wild (LFWA), which has unconstrained changes in posture, lighting, expression, misalignment, occlusion, etc. The LFWA face data set has a total of 13,233 images of 5749 people. Figure 6 shows a face image of a subject in the LFWA database. A subset of 5500 images is selected for this experiment. The training sample consists of 9 samples per person (or more than 9 samples per person) and the remainder are used as a testing sample. Similarly, the alternative training samples are generated using the same methods used for the AR dataset. PCA is utilized to reduce the feature dimensions to 300, 400, and 500 to compare these advanced methods. From Table 3 it is noted that KRDD achieves the highest recognition rate, and the classification time is almost the same as MOST. The recognition rate of KED is the second-best after KRDD. When the feature dimension is at 500, KRDD is 1.7% higher than KED in the recognition rate. MOST is also a KDA-based approach, so it handles suppressing intraclass variations better than other methods. To observe the performance of KRDD more intuitively, the experiment is repeated 10 times with a feature dimension of 600. As shown in Figure 7 among KDA-based methods, KRDD has the best \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Expression** & **Lighting** & **Occlusion** & **Classification Time (s)** \\ \hline SRC [21] & 86.4 & 90.3 & 45.4 & 144 \\ ESRC [22] & 90.8 & 92.4 & 58.1 & 392 \\ RRC [28] & 92.2 & 94.6 & 60.7 & 954 \\ SLF-RKR [27] & 96.8 & 97.7 & 85.5 & 7427 \\ MOST [30] & 97.1 & 96.6 & 81.9 & **64** \\ KED [34] & 98.4 & 98.2 & 94.2 & 81 \\ KRDD & **99.2** & **99.0** & **95.5** & 78 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recognition accuracy and classification time for advanced methods on AR Database with multiple gallery samples per subject. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Expression** & **Lighting** & **Occlusion** & **Classification Time (s)** \\ \hline SRC [21] & 62.4 & 91.4 & 20.8 & 73 \\ ESRC [22] & 87.5 & 91.6 & 68.1 & 171 \\ RRC [28] & 72.6 & 66.7 & 42.5 & 295 \\ SLF-RKR [27] & 33.8 & 26.9 & 21.4 & 1682 \\ MOST [30] & 96.8 & 96.3 & 87.9 & **44** \\ KED [34] & 98.6 & 98.2 & 94.0 & 52 \\ KRDD & **99.3** & **99.0** & **95.2** & 49 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recognition Accuracy and Classification Time for Advanced Methods on AR Database with A Single Gallery Sample Per Subject. Figure 6: Example Images of one Subject on the LFWA Dataset. performance. Compared with SRC, ESRC, RRC, SLF-RKR, MOST and KED, KRDD was improved by an average of 50.5%, 40%, 36.7%, 35.0%, 19.2%, and 2.7%, respectively. We also compare the proposed method with deep learning-based algorithms that require a large amount of additional training data. Such as, ConvNet-RBM [38], GaussianFace [39], DeepID3 [40]. There are also methods that do not require additional training data, such as VMRS [41], Sub-SML [42], and high-dim LBP [43]. Table 4 lists the average recognition rates of advanced algorithms in the LFWA data set. The proposed method achieves a recognition rate of 92.46%, which is better than some advanced methods. Although the proposed method has a lower recognition rate than the deep learning based method, KRDD does not need to collect a large amount of additional training data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **300** & **400** & **500** & **Classification Time (s)** \\ \hline SRC [21] & 38.7 & 39.6 & 40.3 & 152 \\ ESRC [22] & 49.6 & 51.3 & 50.8 & 376 \\ RRC [28] & 50.1 & 52.7 & 53.4 & 829 \\ SLF-RKR [27] & 53.6 & 54.7 & 56.6 & 6844 \\ MOST [30] & 70.4 & 71.3 & 71.8 & **62** \\ KED [34] & 88.5 & 89.2 & 89.8 & 74 \\ KRDD & **90.1** & **90.8** & **91.5** & 70 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The Recognition Rates and Computing Time for Training Dictionaries and Classifying A Testing Sample on the LFWA Database.