## 3 Results and Discussion
### The EYB Eyes Image Results
#### 3.1.3 The DWT with a Curvature-Based Method Results  
The evaluation of the following experiment used the DWT of the _Haar_, _Sym2_, _Db2_, and _Bior2.2_ wavelets. There were two decomposition levels with four wavelet coefficients: approximation (\(A\)), vertical (\(V\)), horizontal (\(Hr\)), and diagonal (\(D\)) details, as explained in Equation (22). Table 5 presents the accuracy results for this experiment. The approximation (\(A\)) showed the best performances among the other coefficients. The second decomposition level overall displayed higher accuracy results. The results reached the peak performance in \(A2\) (approximation coefficient with the second decomposition level) for all wavelets. The best accuracy result was 72.81% from _Sym2_ and _Db2_. We then observed the results from a combination of the DWT and curvature-based method. Each wavelet coefficient was paired with curvatures in every decomposition level, resulting in 16 variables. Figure 5 shows the results for each classifier. Like the previous results, _SVM_-1 demonstrated a higher performance accuracy compared with 1-_NN_ and _SVM_-2. We then further investigated the results using the _SVM_-1 in Table 6. Table 6 shows that the approximation coefficient produced better results, supporting the previous experiment conclusion from Table 5.  
On the other hand, the first decomposition level presented the best results, which is contrary to Table 5, where the second decomposition level produced the best results. The DWT achieved the highest accuracy if paired with \(H\) (mean curvature) or \(X\) (max principal curvature). This argument supported the previous conclusion in Table 4. The best accuracy result (74.65%) was derived from \(A\)-\(H\) (approximation with mean curvature) on the first-level decomposition with _Sym2_ or _Db2_. The combination of the DWT with the curvature-based method improved the accuracy result by 1.84%. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**DWT**} & \multicolumn{8}{c}{**Wavelet Coefficient and Decomposition Level**} \\ \cline{3-10}  & & & \(A1\) & \(V1\) & \(Hr1\) & \(D1\) & \(A2\) & \(V2\) & \(Hr2\) & \(D2\) \\ \hline \multirow{4}{*}{1-NN} & _Haar_ & 58.06 & 50.71 & 51.64 & 20.07 & 55.44 & 58.87 & 56.87 & 29.82 \\  & _Sym2_ & 59.45 & 21.06 & 32.70 & 14.40 & 58.29 & 45.85 & 52.17 & 22.24 \\  & _Db2_ & 59.29 & 21.08 & 32.86 & 14.82 & 58.04 & 45.55 & 51.71 & 22.33 \\  & _Bior2.2_ & 57.24 & 18.39 & 27.60 & 14.95 & 58.27 & 31.24 & 43.80 & 17.65 \\ \hline \multirow{4}{*}{_SVM_-1} & _Haar_ & 67.28 & 58.06 & 55.30 & 41.94 & 70.51 & 56.68 & 54.38 & 43.32 \\  & _Sym2_ & 66.36 & 41.47 & 48.85 & 30.88 & 72.81 & 47.00 & 51.15 & 27.19 \\  & _Db2_ & 66.36 & 41.47 & 48.85 & 30.88 & 72.81 & 47.00 & 51.15 & 27.19 \\  & _Bior2.2_ & 69.12 & 40.55 & 47.00 & 26.27 & 71.43 & 39.17 & 42.86 & 23.50 \\ \hline \multirow{4}{*}{_SVM_-2} & _Haar_ & 16.13 & 33.18 & 36.87 & 30.41 & 56.68 & 44.24 & 37.33 & 29.03 \\  & _Sym2_ & 16.59 & 24.42 & 29.95 & 26.27 & 51.61 & 35.02 & 43.78 & 27.19 \\  & _Db2_ & 16.59 & 24.42 & 29.95 & 26.27 & 51.61 & 35.02 & 43.78 & 27.19 \\  & _Bior2.2_ & 20.74 & 28.11 & 28.57 & 20.74 & 41.47 & 26.73 & 29.95 & 23.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The accuracy (%) results of the DWT on the EYB dataset for the eye images. The performance was calculated for all wavelet coefficients in two decomposition levels, producing \(A1\), \(V1\), \(Hr1\), \(D1\), \(A1\), \(V2\), \(Hr2\), and \(D2\). Figure 5: The accuracy results (%) from **(a)** 1-_NN_, **(b)** _SVM_-1, and **(c)** _SVM_-2. The results were evaluated for a total of 16 variables for wavelet coefficients (_A_, \(V\), _Hr_, and _D_) with 2 decomposition levels and curvature-based methods (_K_, \(H\), \(X\), and _N_).