# 3D Approaches and Challenges in Facial Expression Recognition Algorithms--A Literature Review
## 10 Conclusions  
This survey explores and aims to group and organize all the works and the various methods that tried to face the problem of facial expression recognition through the analysis of human emotions, focusing on 3D solutions. Traditional and deep-learning approaches for facial expression analysis are detailed, with a further distinction between data modality (2D, 3D, and multi-modal 2D+3D), expression granularity (prototypical facial expression and facial action units), and temporal dynamics (still images and image sequences). With this study, we want to provide a guideline for newcomers who will address this topic, and take stock of neural networks, taking advantage of the golden age of AI. The most important works of recent years have been presented, highlighting the pros and cons and the best outcomes in the entire facial expression recognition field.  
The overall recognition accuracy of the expressions is included in a range between 60% and 90%. Some expressions, like anger and fear, generally have the lowest recognition rates. Indeed, the motions of these expressions are moderate compared to happiness or surprise, and thus more challenging to recognize. Regarding the action units, the experiments reached recognition rates in a broader range, between 50% and 95%. The number of features for each AU to be detected will be increased to achieve more accurate results, and the neural networks will gradually be used more and more in the field of facial expression recognition.  
Despite the higher dimensional and computational costs, and the greater difficulty of working in real-time, 3D methods have achieved better recognition rates than the more common 2D methods. For this reason, it is essential to use a dataset that contains 3D facial models or 3D video sequences, such as BU-3 DFE, Bosphorus, BU-4 DFE, D3 DFACS, UPM-3 DFE, BP4D, or a private one. Predicting the expression of the human face in real-time requires recognition as accurately and as quickly as possible, but it becomes quite complicated when compared to the static images because a video is a collection of many frames, not just a single frame. Soon the recognition of emotions in real-time will be beneficial in the field of artificial intelligence research, with the need to recognize as many emotions of different people in one frame and detect mixed emotions. Many researchers developed algorithms that recognize the six basic expressions, but fewer contributions investigated other types of facial expressions or action units. For applications able to work in uncontrolled conditions, further improvements dealing with FER analysis must be done, enlarging the set of facial expressions and considering spontaneous emotions. Reducing the computational time and memory usage are the other two objectives. The primary motivation behind this is the urgent need to have robust and useful applications, which can be used in the real-world.  
In our future work, we plan to include deep learning techniques, working on a private database containing three-dimensional videos and psychological validation of labeled emotions, to perform emotion recognition in the wild.  
Conceptualization, F.M.; methodology, F.N. and F.M.; software, N.D.; validation, F.N., F.M. and N.D.; formal analysis, F.N.; investigation, F.N and N.D.; resources, F.N. and N.D.; data curation, F.N. and N.D.; writing--original draft preparation, F.N. and N.D.; writing--review and editing, F.N. and F.M.; visualization, F.N. and F.M.; supervision, F.M. and E.V.; project administration, E.V.; funding acquisition, E.V.  
This research received no external funding.  
The authors declare no conflict of interest.