## 3 Results and Discussion
### The EYB Eyebrow Image Results
#### 3.2.3 The DWT with Curvature-Based Method Results  
Table 9 displays the results for the eyebrow images using the DWT with the same experimental conditions as the previous eye images from Table 5. The _SVM_-1 showed consistently superior performance, while overall, the second decomposition level still produced a higher accuracy. Contrary to Table 5, the vertical and horizontal coefficients demonstrated improved results. In several cases (e.g., \(H\)\(\underline{r}\)\(\underline{2}\) with \(\underline{Sym2}\) and \(\underline{Db2}\)), the horizontal coefficient displayed a similar or better accuracy than \(A\)\(\underline{2}\) in 1-_NN_ and _SVM_-1. The best results were obtained from \(A\)\(\underline{1}\) with the _Haar_ wavelet, followed by \(A\)\(\underline{1}\), \(A\)\(\underline{2}\), and \(H\)\(\underline{r}\) with _Sym2_ and \(\underline{Db2}\). This \(H\)\(\underline{r}\) accentuated the shapes of eyebrows that had more distinctive  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Octave-Level (O-L)**} \\ \cline{3-8}  & & **0-1** & **0-2** & **0-3** & **1-1** & **1-2** & **1-3** \\ \hline \multirow{4}{*}{\(1\)-_NN_} & _SS_ & 57.76 & 56.36 & 55.30 & 52.24 & 49.95 & 45.18 \\  & _SS + K_ & 47.74 & 48.16 & 39.52 & 35.37 & 38.69 & 37.35 \\  & _SS + H_ & 82.70 & 83.11 & 83.25 & 76.82 & 76.04 & 70.90 \\  & _SS + X_ & 83.76 & 91.00 & 83.41 & 69.12 & 64.88 & 61.45 \\  & _SS + N_ & 84.22 & 83.00 & 82.53 & 79.26 & 74.72 & 67.12 \\ \hline \multirow{4}{*}{_SVM_-1} & SS & 87.56 & 88.48 & 87.10 & 85.25 & 86.64 & 86.18 \\  & _SS + K_ & 78.80 & 82.95 & 83.87 & 64.52 & 65.44 & 65.44 \\  & _SS + H_ & 93.55 & 94.47 & 89.86 & 94.93 & 93.55 & 85.71 \\  & _SS + X_ & 94.01 & 93.09 & 91.24 & 88.94 & 88.48 & 82.03 \\  & _SS + N_ & 93.55 & 94.93 & 93.09 & 93.55 & 89.40 & 83.41 \\ \hline \multirow{4}{*}{_SVM_-2} & SS & 66.36 & 47.47 & 55.30 & 29.49 & 22.58 & 23.04 \\  & _SS + K_ & 36.87 & 39.63 & 44.70 & 21.20 & 34.10 & 23.50 \\ \cline{1-1}  & _SS + H_ & 63.59 & 57.14 & 38.25 & 31.34 & 32.26 & 33.64 \\ \cline{1-1}  & _SS + X_ & 46.08 & 67.74 & 30.41 & 28.11 & 27.19 & 22.12 \\ \cline{1-1}  & _SS + N_ & 62.21 & 55.76 & 57.60 & 38.71 & 40.09 & 33.64 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The detailed accuracy (%) results of the scale-space method and its combination with a curvature-based method (_SS_, _SS + K_, _SS + H_, _SS + X_, and _SS + N_) on the EYB dataset for the eyebrow images.  
Figure 6: Combination results of (**a**) _SS + K_, (**b**) _SS + H_, (**c**) _SS + X_, and (**d**) _SS + N_ with \(O=1\) and \(L=1\) for an eyebrow image.  
Table 10 displays the results of the DWT with a curvature-based method for the eyebrow images. We only presented the results with _SVM_-1 since this classifier repeatedly showed higher results than 1-_NN_ and _SVM_-2. The first decomposition level demonstrated higher performance, as shown in Table 6. The curvatures \(H\), \(X\), and \(N\) were unsurpassed if combined with the wavelets. Accordingly, the best accuracy was 98.61%, obtained from \(A\)-\(H\) with the _Haar_ wavelet from the first decomposition level, followed by 98.16% from the _Sym_2 and _Db_2 wavelets. This result achieved the highest accuracy thus far. The combination of the DWT with a curvature-based method improved the accuracy result by 6.45%.  
Figure 8 displays the results when using the _Sym_2 wavelet from the first decomposition level's combined curvature, showing \(A\)-\(K\) (Figure 8a), \(A\)-\(H\) (Figure 8b), \(A\)-\(X\) (Figure 8c), and \(A\)-\(N\) (Figure 8d). We can observe why \(A\)-\(H\), \(A\)-\(X\), and \(A\)-\(N\) performed well, but \(A\)-\(K\) showed a lesser result. \(A\)-\(K\) only captured inferior features, which yielded a worse performance in the recognition system.