## 4 Results
### Execution Time Comparison  
Besides outperforming in terms of various performance measures, the proposed approach, PA_B-J48, also exhibits better time efficiency. PA_B-J48 is {1.2, 10.5, 0.5, and 0.6} times faster than EM-Stat, Ali, TA, and AM-Mouth, respectively, when experimented on \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **BNB** & **BAda** & **BNN** & **BSVM** & **PA\_B-J48** \\ \hline DrivFace & 81.56 & 92.86 & 92.17 & 89.21 & **94.43** \\ \hline BU & 69.42 & 85.97 & **92.08** & 83.81 & **92.08** \\ \hline FT-UMT & 94.06 & 95.25 & 95.64 & 94.65 & **96.63** \\ \hline Pointing’04 & 80.61 & 82.37 & **95.02** & 81.93 & 83.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the proposed approach, PA_B-J48, with its variants, i.e., BNB, BAda, BNN and BSVM for the DrivFace, BU, FT-UMT, and Pointing’04 datasets in terms of percentage accuracy. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **EM-Stat** & **TA** & **AM-Mouth** & **Ali** & **Lee** & **Frid** & **PA\_B-J48** \\ \hline A (\%) & **82.81** & 67.40 & 72.24 & **82.81** & 81.49 & 81.05 & **82.81** \\ \hline Se (\%) & **82.00** & 67.00 & 72.00 & **82.00** & 81.00 & 81.00 & **82.00** \\ \hline P (\%) & **83.00** & 71.00 & 74.00 & **83.00** & 81.00 & 82.00 & **83.00** \\ \hline F & **0.83** & 0.67 & 0.72 & **0.83** & 0.81 & 0.81 & **0.83** \\ \hline TP & **0.82** & 0.67 & 0.72 & **0.82** & 0.81 & 0.81 & **0.82** \\ \hline FP & 0.16 & **0.28** & 0.25 & 0.17 & 0.23 & 0.17 & 0.16 \\ \hline ROC & **0.88** & 0.73 & 0.78 & 0.86 & 0.84 & 0.80 & **0.88** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the existing approaches, EM-Stat, TA, AM-Mouth, Ali, Lee and Frid with PA_B-J48 using the Pointing’04 dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **BNB** & **BAda** & **BNN** & **BSVM** & **PA\_B-J48** \\ \hline DrivFace & 81.56 & 92.86 & 92.17 & 89.21 & **94.43** \\ \hline BU & 69.42 & 85.97 & **92.08** & 83.81 & **92.08** \\ \hline FT-UMT & 94.06 & 95.25 & 95.64 & 94.65 & **96.63** \\ \hline Pointing’04 & 80.61 & 82.37 & **95.02** & 81.93 & 83.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the proposed approach, PA_B-J48, with its variants, i.e., BNB, BAda, BNN and BSVM for the DrivFace, BU, FT-UMT, and Pointing’04 datasets in terms of percentage accuracy. the DrivFace dataset, as shown in Figure 8. One reason for its better time efficiency is that it uses simple and quicker-to-compute features, i.e., ratios and variances. For example, rather than taking length and width of eyes as features, it only takes their ratios. EM-Stat not only computes geometric features, including the height of eyes and the mouth, but it also calculates the relationship between them to predict distraction that results in reduced time efficiency. Ali uses a neural network classifier that results in lower time efficiency compared to the proposed approach. TA and AM-Mouth uses fewer features that are simple and quicker to compute. In a few cases, it is also a trade-off between percentage accuracy and time efficiency. [PERSON] and [PERSON] show better time efficiency, but their performance degrades significantly in terms of percentage accuracy, sensitivity, precision, F-measure, TP-Rate, FP-Rate, and ROC-Area on the DrivFace, BU, FT-UMT, and Pointing'04 datasets. In the case of the DrivFace dataset, the percentage accuracy of [PERSON] and [PERSON] is 88.52% and 91.30%, respectively, while PA_B-J48 outperforms these techniques and exhibits an accuracy of 94.43%. A similar trend of time efficiency is shown in the other three datasets, including BU, FT-UMT, and Pointing'04. PA_B-J48 is 9.9, 1.3, 0.4, and 0.3 times faster than Ali, EM-Stat, TA, and AM-Mouth, respectively, in the BU dataset, while it is 8.5, 0.7, 0.2, and 0.3 times faster than the similar approaches in the case of the FT-UMT dataset. In the case of Pointing'04, PA_B-J48 is 8.6, 0.8, 0.25, and 0.45 times faster than Ali, EM-Stat, TA, and AM-Mouth, respectively.