# Machine Learning-Based Lie Detector Applied to a Novel Annotated Game Dataset
## 5 Experiments
### Generalized Models
#### 5.1.2 Experiment Results of Classical Machine Learning  
Table 3 shows the five-fold cross validations accuracy and F1-score from LBP descriptors combined with several classifiers (SVM, AdaBoost and LDA). We can see that the best results were obtained with the use of Adaboost, reaching 52.6% accuracy and a 52 F1-score (indicated with bold face in respective table), which is better than those of using other classifiers, such as SVM and LDA. Furthermore, in general, we notice that the use of 12 points of neighboring (i.e., P = 12), and dividing the image with 2 \(\times\) 2 grid values produce the best results. This suggests that modest values of parameters are advantageous to improve the lie estimates.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**LBP**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**AdaBoost**} & \multicolumn{2}{c}{**LDA**} \\ \cline{2-7}  & **ACC** & **F1** & **ACC** & **F1** & **ACC** & **F1** \\ \hline
1 \(\times\) 1 Grid \(-\) P = 8 & 48.8 & 46.4 & 48.8 & 48.6 & 47.8 & 45.2 \\
2 \(\times\) 2 Grid \(-\) P = 8 & 50 & 46 & 50.2 & 49.6 & 50.2 & 4  
We can see the results of HOG descriptor on the Table 4, that is obtained using similar five-cross validation settings. We can see a similar pattern with the results from LBP, where using the 8 \(\times\) 8 grid size with the modest value of 2 \(\times\) 2 block cells to compute the histogram produces better results. Furthermore, we note that the best accuracy is achieved by AdaBoost, achieving the accuracy 53% and 52.8 F1-score, respectively.  
Finally, the results obtained for SIFT descriptors can be seen in Table 5 with the varying number of the bag of words (BoW\(\sim\)K). Here, we found that in general, the use of a K value of 800 is beneficial. Furthermore, using AdaBoost classifier achieves the maximum results with an accuracy of 53% and a F1 value of 52.2.  
Figure 6 shows the examples of TP, FP, TN and FN of each best performer of the classical machine learning models. Notice that the facial expressions are quite similar across the examples, with slight changes happening in the mouth area in the case of both being correctly classified as lies (TP and FN). However, on the failed recognition (FP and FN), the facial area is mostly neutral, which may thus confuse the proposed methods in their predictions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**HOG**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**AdaBoost**} & \multicolumn{2}{c}{**LDA**} \\ \cline{2-7}  & **ACC** & **F1** & **ACC** & **F1** & **ACC** & **F1** \\ \hline \(8\times 8\) Grid \(-1\times 1\) Cells & 50.4 & 50.2 & 50.2 & 50 & 49.6 & 49.4 \\ \(8\times 8\) Grid \(-2\times\)2 Cells & 51.2 & **51** & **53** & **52.8** & **51** & **51** \\ \hline \(16\times 16\) Grid \(-1\times 1\) Cells & **52** & 50.4 & 49 & 49 & 48.8 & 48.8 \\ \(16\times 16\) Grid \(-2\times 2\) Cells & 51 & 49.8 & 51.4 & 51.2 & 48.6 & 48.6 \\ \hline AVG & 51.15 & 50.35 & 51.4 & 51.2 & 48.6 & 48.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy and F1 for SVM, AdaBoost and LDA for HOG descriptors depending on cells’ size (8\(\times\)8, and 16\(\times\)16) and the blocks’ size (1\(\times\)1, and 2\(\times\)2). Note that the bold face numbers indicate the optimum values on respective metric.  
Figure 6: Images examples of correctly and incorrectly classified samples for using handcrafted based features.