# Detecting Deceptive Behaviours through Facial Cues from Videos: A Systematic Review
###### Abstract  
Interest in detecting deceptive behaviours by various application fields, such as security systems, political debates, advanced intelligent user interfaces, etc., makes automatic deception detection an active research topic. This interest has stimulated the development of many deception-detection methods in the literature in recent years. This work systematically reviews the literature focused on facial cues of deception. The most relevant methods applied in the literature of the last decade have been surveyed and 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.  
deception detection; facial cues; dataset; feature extraction; decision-making algorithms +
Footnote †: journal: Medical  
Detecting deceptive behaviours from explicit cues makes deception harder because explicit cues are easier to manipulate through carefully chosen language and wording, while implicit cues, even if also subject to manipulation, are more spontaneous and challenging to control consciously [11]. On the other hand, the interpretation of implicit cues tends to be more subjective and context-dependent, requiring a deeper understanding of individual differences and cultural norms, while explicit cues are typically more straightforward and easier to analyse in isolation. Finally, detection deception using implicit cues requires collecting data through video recordings or specialised sensors to capture non-verbal behaviours, which is more challenging compared to collecting text data for explicit cues.  
Considering the above-mentioned differences, the solutions provided in the literature for detecting deceptive behaviours have different characteristics if relying on explicit or implicit cues. The systematic literature review provided in this paper deals with implicit cues and, in particular, focuses on a specific type of implicit cues, i.e., facial cues. Specifically, the most relevant deception-detection systems focusing on facial cues have been described and classified considering the methods used in the main steps of the facial-deception-detection process (i.e., video pre-processing, facial feature extraction, and decision making). The choice to focus on facial cues is justified by the results of several existing studies [12; 13] arguing that facial cues have an important role in detecting deceptive behaviours due to their involuntary aspects. As stated by [PERSON] in his \"inhibition hypothesis\" [13], some actions of the facial muscles are the hardest to be inhibited due to the difficulty to control them voluntarily.  
The main contributions of this review are as follows:  
1. to collect and systematise the scientific knowledge related to automated face deception detection from videos;
2. to summarise (i) the methods used for video pre-processing, (ii) the facial extracted features, (iii) the decision-making algorithms, and (iv) the datasets used for the evaluation;
3. to point out future research directions that emerged from the analysis of the existing studies.  
The paper is structured as follows. Section 2 introduces the related work. Section 3 illustrates the methodology followed for the systematic literature review, while in Section 4, the results are discussed. Section 5 describes how the surveyed studies answered the review questions defined in the paper. Section 6 summarises future research directions that emerged from the literature review. Finally, Section 7 concludes the paper.