## 3 Results and Discussion
### The EYB Eyebrow Image Results
#### 3.2.4 Summary of the EYB Eyebrow Image Results  
The following includes the findings based on Section 3.2. First, we found that the eyebrow images yielded higher accuracy results compared with the eye images. The EYB dataset has variations in the lighting angle. These high results proved the argument in [26] that the eyebrows are robust against illumination. Second, similar to the eye results, the best combination was with the mean curvature and max principal curvature. In addition, in the eyebrow images, the min principal curvature also produced a high accuracy. The highest accuracy was 98.61% from \(A+H\) with the Haar wavelet and 98.16% from \(A+H\) with the _Sym_2 and _Db_2 wavelets. Both were derived using the first decomposition level. The mean, max, and min principal curvatures successfully characterized the multiscale eyebrow images to be more distinctive. Third, we found that the horizontal approximations in the DWT and its combination with a curvature-based method produced high accuracy results. We did not find these high results when using the eye images. The horizontal approximation produced high accuracy because the shape of the eyebrows might have had more distinctive features in the horizontal direction. Fourth, the decreasing results due to the smaller resolutions of the images also happened in the eyebrow images. Fifth, the SVM linear classifier was consistently showing the highest results. This repeated performance also occurred with the eyebrow images.  
Figure 8: _Sym_2 wavelet from the first decomposition level combined with curvature-based method: (a) \(A\)-\(K\), (b) \(A\)-\(H\), (c) \(A\)-\(X\), and (d) \(A\)-\(N\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**DWT**} & \multicolumn{10}{c}{**Wavelet Coefficient and Curvature Combination**} \\ \cline{3-14}  & & _A-K_ & _A-H_ & _A-X_ & _A-N_ & _V-K_ & _V-H_ & _V-X_ & _V-N_ & _H-K_ & _H-H_ & _H-X_ & _H-N_ & _D-K_ & _D-H_ & _D-X_ & _D-N_ \\ \hline \multirow{4}{*}{_SVM-1_} & Har Lvl & 38.25 & 98.61 & 95.39 & 96.77 & 37.79 & 90.78 & 87.56 & 89.86 & 40.09 & 92.63 & 92.63 & 91.71 & 47.93 & 80.65 & 82.95 & 82.95 \\ \cline{2-14}  & Har Lv2 & 34.10 & 88.48 & 82.95 & 87.56 & 33.64 & 92.17 & 82.49 & 88.02 & 30.88 & 90.78 & 89.86 & 84.79 & 27.19 & 71.43 & 59.91 & 65.44 \\ \cline{2-14}  & Sym2 LV1 & 43.78 & 98.16 & 96.31 & 97.24 & 43.32 & 88.48 & 84.79 & 85.25 & 42.40 & 94.01 & 94.01 & 91.24 & 48.39 & 72.81 & 76.96 & 69.99 \\ \cline{2-14}  & Sym2 LV2 & 41.94 & 94.01 & 86.64 & 94.93 & 30.41 & 82.03 & 84.33 & 81.11 & 35.94 & 92.63 & 88.94 & 92.63 & 27.19 & 70.97 & 60.37 & 65.90 \\ \cline{2-14}  & D02 LV1 & 43.78 & 98.16 & 96.31 & 97.24 & 43.32 & 88.48 & 84.79 & 85.25 & 42.40 & 94.01 & 94.01 & 91.24 & 48.39 & 72.81 & 76.96 & 69.59 \\ \cline{2-14}  & D02 LV2 & 41.94 & 94.01 & 86.64 & 94.93 & 30.41 & 82.03 & 84.33 & 81.11 & 35.94 & 92.63 & 88.94 & 92.63 & 27.19 & 70.97 & 60.37 & 65.90 \\ \cline{2-14}  & Bur22 Lv1 & 34.56 & 96.77 & 96.77 & 97.24 & 40.09 & 87.10 & 79.26 & 83.41 & 35.94 & 93.09 & 90.78 & 93.09 & 44.70 & 68.20 & 70.97 & 67.74 \\ \cline{2-14}  & Bur22 Lv2 & 34.56 & 93.09 & 89.40 & 94.01 & 29.49 & 72.81 & 67.28 & 69.12 & 32.72 & 90.78 & 90.32 & 87.10 & 26.73 & 52.07 & 49.27 & 51.61 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The accuracy (%) results of combined DWT with a curvature-based method on the EFB dataset for the eyebrow images using _SVM-1_. The combined methods produced wavelet coefficients with two decomposition levels and curvature-based variables (e.g., _A-K_ (approximation with Gaussian curvature) until _D-N_ (diagonal with min principal curvature)).