# Detecting Deceptive Behaviours through Facial Cues from Videos: A Systematic Review
## 5 Discussion
### RQ3: Which Decision-Making Algorithms for Automated Deception Detection Are Used? During the decision-making step, the extracted features are used to train a classification approach to automatically distinguish between deceptive and truthful videos. Specifically, 20 studies among the surveyed ones apply a deception classification approach, as represented in Table 7. Most of them (19 studies; 95%) use supervised learning techniques that require labelled training data (i.e., a set of feature vectors with their corresponding labels (deceptive vs. truthful)). The most applied supervised technique is the support vector machine (SVM), which is applied in seven studies (35%), followed by six studies (30%) applying Random Forest, five studies applying neural networks (25%), and four studies (20%) applying K-nearest neighbourhood. Various types of regression models have been experimented by surveyed papers on facial deception detection, including multinomial logistic and multivariate regression. Only one study relies on unsupervised methods that do not need manually annotated facial attributes to train the model. Specifically, it applies hidden Markov models [42]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**2D**} & \multirow{2}{*}{**3D**} & \multicolumn{2}{c}{**A Algorithm**} & \multirow{2}{*}{**Software Tool**} & \multicolumn{2}{c}{**Algorithm**} & \multirow{2}{*}{**Software Tool**} \\ \cline{5-6} \cline{8-8}  & & & **Feature-Based** & & **Image-Based** & & **Feature-Based** & **Image-Based** \\ \hline [PERSON] and [PERSON], 2023 [24] & X & X & \begin{tabular}{c} Viola-Jones face \\ detector [33] \\ \end{tabular} & & & \begin{tabular}{c} Constrained Local Neural \\ Field (CLNP) [46] \\ \end{tabular} & \\ \hline [PERSON] et al., 2021 [52] & & & & & & \\ [PERSON] et al., 2022 [27] & & & & & & \\ [PERSON] and [PERSON], 2020 [32] & & & & & & \\ [PERSON] et al., 2020 [55] & X & & & & & OpenFace [47] & OpenFace [47] \\ [PERSON], 2017 [57] & & & & & & \\ [PERSON] and [PERSON], 2021 [99] & & & & & & \\ [PERSON] et al., 2018 [14] & & & & & & \\ \hline [PERSON] and [PERSON], 2016 [28] & X & & & FitPart SDK [52] & & FitPart SDK [52] \\ [PERSON] and [PERSON], 2014 [30] & X & \begin{tabular}{c} [PERSON] face \\ detector [33] \\ \end{tabular} & & \begin{tabular}{c} Constrained Local Neural \\ Field (CLNP) [46] \\ \end{tabular} \\ \hline [PERSON] et al., 2021 [31] & X & & & \begin{tabular}{c} ‘dlib’ library [50] \\ \end{tabular} & & \begin{tabular}{c} FAN [54] \\ \end{tabular} \\ \hline [PERSON] et al., 2014 [33] & X & \begin{tabular}{c} Active Shape Model \\ (ASM) tracker [43] \\ \end{tabular} & & \begin{tabular}{c} Active Shape Model \\ (ASM) tracker [43] \\ \end{tabular} & & \\ \hline [PERSON] et al., 2020 [34] & X & & \begin{tabular}{c} ‘dlib’ library [50] \\ \end{tabular} & & ‘dlib’ library [50] \\ \hline [PERSON] et al., 2015 [40] & X & & & \
\begin{tabular}{c} InsightFace [53] \\ \end{tabular} & &
\begin{tabular}{c} InsightFace [53] \\ \end{tabular} \\ \hline [PERSON] et al, 2015 [42] & X & & & OpenCVlib [51] & & FaceSDK [49] \\ \hline [PERSON] et al., 2017 [43] & X & & & IntraFace [48] & IntraFace [48] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Algorithms and software tools applied for face detection and landmark tracking by surveyed papers.