# Detecting Deceptive Behaviours through Facial Cues from Videos: A Systematic Review
## 6 Future Research Directions
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Reference** & **Future Research Directions** & **Aim of the Future Research** \\ \hline [PERSON] and [PERSON], 2016 [28] & - & Integrating verbal, non-verbal, and facial cues for higher accuracy. & - & Multimodal approach \\  & - & Extending the size of the database & - & Dataset extension \\ \hline [PERSON] and [PERSON], 2020 [32] & - & Developing affect-aware systems for automatically detecting deception and other social behaviours, particularly those occurring in unconstrained situations in the wild. & - & Development of further methods \\ \hline [PERSON] et al., 2014 [33] & - & Generalization to the greater sample population and to other scenarios. & - & Larger samples \\  & - & Incorporating 3D deformable models. & - & Transferability \\ \hline [PERSON] et al., 2020 [34] & - & Evaluating the models on real-world multimodal deception data. & - & Model validation \\  & - & Exploring more complex classifiers to model conversational context and time-dependent features. & - & Development of further methods \\ \hline [PERSON] et al., 2022 [36] & - & Extending the dataset with equal representation by gender and race and with a large number of videos. & - & Dataset extension \\ \hline [PERSON], 2017 [37] & - & Utilizing an individual’s expression during specific baseline mental states. & - & Larger gender/race-matched samples \\ \hline [PERSON] et al., 2023 [38] & - & Developing a fully autonomous deception detection system by combining other modalities, such as vocal and text. & - & Multimodal approach \\ [PERSON] et al., 2023 [38] & - & Increasing video features using hand gestures, head movements, and body posture. & - & Increasing the features \\  & - & Reducing the complexity for real-time deception detection. & - & Complexity reduction \\ \hline [PERSON] et al., 2015 [40] & - & Using automatic facial and gesture identification and automated speech transcription for real-time deception detection. & - & Multimodal approach \\ \hline [PERSON] et al., 2015 [42] & - & Using less expensive features or more powerful hardware for real-time use. & - & Development of further methods \\  & - & Increasing the number of videos on the database. & - & Increasing the features \\ \hline [PERSON] et al., 2018 [44] & - & Exploring ways to automatically learn the best views combination for the classification task for reducing the complexity. & - & Complexity reduction \\ \hline \end{tabular}
\end{table} 
Table 10: _Cont._in the performance of the algorithms, while the integration of several modalities yields higher accuracy. Relevance is also given to the complexity reduction (four studies; 29%) by exploring innovative ways of combining and classifying the features for real-time deception detection. Furthermore, a look at the development of further methods and the use of larger samples (three studies; 21% each) is also suggested by the surveyed studies. In more detail, the need to develop more accurate methods of action unit detection, more complex classifiers to model conversational context and time-dependent features, and affect-aware systems resulted from the analysis. On considering the use of larger samples, particular attention is given to an equal representation by gender and race and generalization to other scenarios. Finally, it is also important to mention the future research directions looking for further psychological analysis, transferability, and increase in the extracted features (two studies; 14% each), as well as the explainability of the algorithms, model validation, and use of 3D deformable models (one study; 7% each).