# Detecting Deceptive Behaviours through Facial Cues from Videos: A Systematic Review
## 2 Related Work  
Automated deception detection from videos is a challenging task that can find applications in many real-world scenarios, including airport security screening, job interviews, court trials, and personal credit risk assessment [14]. Various surveys of automated deception-detection methods from videos have been developed in the literature, classifying the methods according to different dimensions. In particular, recent surveys (see Table 1) have provided a very broad overview of deception-detection methods considering monomodal and multimodal features. The survey developed by [PERSON] et al. [15] provides a systematic literature review of deception detection based on machine-learning methods, underlying the techniques and approaches applied, the difficulties, the kind of data, and the performance levels achieved. In this survey, verbal and non-verbal cues, such as facial expressions, gestures, and prosodic and linguistic features, have been considered. The 81 surveyed papers are classified according to the type of extracted features (emotional, psychological, and facial) and the kinds of machine-learning algorithms. Analogously, the survey in [16] gives an overview of automated deception detection through machine-intelligence-based techniques by providing a critical analysis of the existing tools and available datasets. The authors focused on deception detection through text, speech, and video data analysis by classifying the 100 surveyed papers according to both the research domain (i.e., psychological, professional, and computational)and the type of extracted features (verbal, non-verbal, and multimodal). A further survey focused on monomodal features (speech cues) is proposed by [PERSON] and [PERSON] [17], in which deep-learning-based techniques, available datasets, and metrics extracted from the surveyed papers are discussed. Different from existing surveys, this work focuses on deception detection methods specifically based on facial cues and proposes a classification of these methods based on the main steps of the facial deception detection process (video pre-processing, facial feature extraction, and decision making), as proposed by [PERSON] et al. [18]. On the contrary, the existing surveys considered only the second and third steps of this process. A general workflow of the followed deception detection process that used video datasets is provided in Figure 1.  
During the video pre-processing step, the video is analysed by a face detector, which bounds the box containing the face, and by a facial-landmark-tracking tool, which locates and tracks the facial landmarks. In the facial-feature-extraction step, the extraction of a set of facial features for recognizing the facial cues meaningful to deception is carried out. Finally, the classification of the extracted features into truthful or deceptive behaviour is performed in the decision-making step. According to these steps, the studies included in the review are analysed to extract (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.