# Detecting Deceptive Behaviours through Facial Cues from Videos: A Systematic Review
## 7 Conclusions  
This study provided a systematic literature analysis on facial deception detection in videos. The main contribution was a discussion of the methods applied in the literature of the last decade classified according to the main steps of the facial-deception-detection process (video pre-processing, facial feature extraction, and decision making). Moreover, datasets used for the evaluation and future research directions have also been analysed. The initial search on WoS and Scopus databases returned 230 studies, of which 38 studies were retained after the application of the inclusion and exclusion criteria defined in the methodology. Finally, 24 studies were included in the qualitative synthesis according to the scores obtained in the quality evaluation checklist. The analysis revealed that in the last ten years, the interest of the scientific community in the topic of facial deception detection began growing in 2014 and reached a peak in 2020. Considering the methods for video pre-processing, we observed that the majority of the surveyed studies use academic and commercial software for face detection and landmark tracking. Moreover, the most applied and successful feature-based methods are the active shape model [43] and the Viola-Jones method [33], while the Constrained Local Neural Field (CLNF) [46] is the only used image-based approach. Concerning the extracted facial features, the Facial Action Coding System (FACS) is the most used system to represent all visually discernible facial movements, applied by 68% of the studies. Moreover, 82% of the studies rely on extracting facial expression features, followed by head pose (50% of the studies) and gaze motion (41%). Concerning the decision-making algorithms, 95% of the studies use supervised learning techniques, mainly the support vector machine (SVM) (35% of the studies), followed by random forest (30%), neural networks (25%), and K-nearest neighbourhood (20%). Furthermore, the real-life trial dataset [59] is the most popular dataset used for the evaluation of the performance of the decision-making algorithms (33% of the studies), while the remaining studies (67%) developed their own dataset mainly by recording videos from game/TV shows (24%), using spontaneous (19%) or controlled (19%) interviews, and using storytelling (10%). Finally, the surveyed studies highlight the following future research directions: (i) extension of the current datasets for evaluating the detection algorithms; (ii) use of a multimodal approach that combines different types of features (facial, speech, video, text, hand gestures, etc. ); (iii) reduction in the complexity of the detection process for real-time use; (iv) use of larger samples for an equal representation by gender and race; and (v) development of further more accurate and affect-aware methods. The study has some limitations mainly related to the focus on facial cues from videos. Future studies could also include analysis of explicit cues (verbal communication) from face-to-face interactions. Indeed, multimodal analysis that combines both verbal and non-verbal cues is the most valuable approach for detecting deception [15]. Conceptualization, A.D. ([PERSON]), A.D. ([PERSON]), P.G. and F.F. ; Methodology, A.D. ([PERSON]), A.D. ([PERSON]), and P.G. ; Validation, A.D. ([PERSON]) and A.D. ([PERSON]); Formal analysis, A.D. ([PERSON]) and A.D. ([PERSON]); Investigation, A.D. ([PERSON]), A.D. ([PERSON]), and P.G. ; Data curation, A.D. ([PERSON]), A.D. ([PERSON]), and F.F. ; Writing--original draft preparation, A.D. ([PERSON]) and A.D. ([PERSON]); Writing--review and editing, A.D. ([PERSON]) and A.D. ([PERSON]); Supervision, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available upon request from the corresponding author. The authors declare no conflict of interest.