# 3D Approaches and Challenges in Facial Expression Recognition Algorithms--A Literature Review
## 4 Facial Animation  
Facial animation is an area of computer graphics that consists of methods and techniques for generating and animating models of a human, an animal, or a fantasy character face. [PERSON] made the first efforts to represent and animate three-dimensional faces using computers in 1972 [121]. Computer-based facial expression modeling and character animation is not a new endeavor but has been considerable growth of interest in recent years.  
Following the success for describing movements of facial muscles of the FACS and Action Units developed by [PERSON] and [PERSON] in 1978 [20], [PERSON] [122] and [PERSON] [123] in the early-1980s produced, respectively, the first physically based muscle-controlled face model, and techniques for facial caricatures. Their studies gave birth to the first animated human character able to express emotion through facial expressions and body movements.  
Different techniques exist for the generation of facial animation data: marker-based motion capture [124; 125], markerless motion capture, audio-driven, and keyframe animation. Marker-based techniques are widely used for real-time facial animation thanks to their robustness but are not useful for retrieving fine-scale dynamics and require specialized sensors. To simplify the motion capture process, techniques without requiring markers or specialized tracking hardware came out leveraging depth sensors and structured-light based devices [126]. The researchers demonstrated the ability to track detailed facial expressions from a 3D sensor in real-time, but the system required an extensive set of pre-processed facial expressions and consequently a lengthy training session. The year later, [PERSON] et al. [127] used the same system replacing the Principal Component Analysis (PCA) model with an optimized rig, in order to reduce training poses and enable retargeting.  
Real-time 3D low-cost sensors such as Microsoft's Kinect favored the development of new methodologies that simplify the procedure [128; 129]. In [128], a user-specific dynamic expression model is created in an offline preprocessing step. The novel face tracking algorithm combines 2D color image and 3D depth map, simultaneously captured by Kinect, in a systematic way with user-specificblendshapes. This proposed method achieves high-quality performance-driven facial animation in real-time with a low-cost and markerless acquisition system, and more robust and accurate results than previous video-based methods. [PERSON] et al. [129] proposed a real-time facial animation system with adaptive tracking without any training or expression calibrations, achieving superior tracking fidelity than existing state-of-the-art techniques.  
Other works in facial animation are [130; 131; 132]. [PERSON] and [PERSON] [130] presented in their paper a novel mesh deformation method to automatically transfer facial blendshapes from a reference to a source face model. Parameters such as elasticity, mesh curvature descriptors were not considered, but the presented method achieves a lower error rate than the previous methodologies. [PERSON] and [PERSON] [131] studied speech animation in real-time based on live speech input, synthesizing but maintaining the realism of facial animation. [PERSON] et al. [132] presented a methodology that provides the ability to efficiently transfer facial animations to face models with different morphological variations.  
Recently, [PERSON] and [PERSON] [133; 134] presented a \"complete pipeline to photo-realistically transform the facial expression for monocular video in real-time\" [133] and a \"real-time, automatic, geometry-based method for capturing fine-scale facial performance from monocular RGB video\" [134]. Facial animation applications include communication, education, and scientific simulation area, even if the primary use remains animation films and computer games.