# Detecting Deceptive Behaviours through Facial Cues from Videos: A Systematic Review
## 5 Discussion
### RQ1: Which Methods for Video Pre-Processing Are Used? Pre-processing consists of those operations that prepare data for subsequent analysis in an attempt to compensate for systematic errors. The videos are subjected to several corrections, such as geometric, radiometric, and atmospheric, although all these corrections might not be necessarily applied in all cases. These errors are systematic and can be removed before they reach the user. According to the nature of the information to be extracted from the videos, the most relevant pre-processing techniques are chosen. Facial region detection, landmark localisation, and tracking are necessary for pre-processing when capturing videos in unconstrained recording conditions. This step has the aim of localizing and extracting the face region from the background and identifying the locations of the facial key landmark points. These points have been defined as \"either the dominant points describing the unique location of a facial component (e.g., eye corner) or an interpolated point connecting those dominant points around the facial components and facial contour\" [44]. Regardless of the application domain, face detection and landmark tracking from input video streams are preliminary steps necessary to process any kind of information extractable from the face. Over the past few decades, several face-detection and landmark-tracking algorithms have been developed [33; 43; 45; 46], as well as academic or commercial software that performs joint face detection and landmark localization [47; 48; 49; 50; 51; 52; 53; 54]. The vast majority of these methods and tools are based on 2D image processing. The main drawback of 2D face-detection and landmark-tracking methods is that their performances depend strictly on the following four factors: the pose of the head that can generate self-occlusions and deformations; occlusions caused by masks, sunglasses, or other faces; illumination variations caused by internal camera control and skin reflectance properties; time delay caused by face changes over time. However, these methods have the advantages of being generally easier and less expensive in terms of computational load compared to 3D face-detection methods. This last class of methods uses a 3D dataset that contains both face and head shapes as range data or polygonal meshes [55]. The 3D methods allow the fundamental limitations of 2D methods to be overcome since they are insensitive to pose and illumination variations. A widely accepted classification of face-detection techniques [56] distinguishes them into two categories: feature-based and image-based approaches. On the one hand, feature-based techniques make explicit use of face knowledge by extracting local or global features (e.g., eyes, nose, mouth) and use this knowledge to classify between facial and non-facial regions. The active shape model (ASM) [38] and the Viola-Jones method [33] are two of the most applied and successful feature-based methods so far. On the other hand, image-based approaches learn to recognise a face pattern from examples following a training step for classifying examples into face and non-face prototype classes. The only image-based approach, applied by the surveyed studies, is the Constrained Local Neural Field (CLNF) [46]. There is also academic and commercial software that performs joint face-detection and landmark localization, such as OpenFace [47], FaceSDK [49], IntraFace [48], 'dib' library [50], OpenCV [51], PittPatt SDK [52], InsightFace [53], and FAN [54]. A total of 18 papers among those surveyed (24) use the face-detection and landmark-tracking methods represented in Table 5.  
From the table, we can observe that during the video pre-processing step, mainly 2D methods are used (17 studies; 94%), while only two works apply 3D methods. Moreover,the majority of the studies (14 studies; 78%) apply academic and commercial software that allows joint face-detection and landmark localization tasks to be performed.