# 3D Approaches and Challenges in Facial Expression Recognition Algorithms--A Literature Review
## 9 Discussion
### Next Steps  
Considering the different approaches analyzed in the paper, the highest recognition rates were obtained by working on three-dimensional data, or with multi-modal algorithms using 2D and 3D data. These results confirm the advantages of using 3D images or videos compared to the most common \begin{table}
\begin{tabular}{l c c} \hline \hline
**Comparisons** & **2D** & **3D** \\ \hline
**Illumination changes, head** & 2D images and videos suffer from these variations, which can affect performance & 3D data is naturally robust to these variations, immune to illumination and to some extent to pose variations \\
**Data acquisition** & Trivial acquisition, possible with any device & Technology makes 3D acquisition easier and easier \\
**Amount of data available** & Large amount of data and public datasets & Only a few datasets are available but are meant to increase \\
**Dimensional and computational costs** & Very low costs & Greater dimensionality and, consequently, higher storage and computational costs \\
**Facial surface measurements** & Not enabled, it is a difficulty inherent in 2D modality & 3D enables true facial surface measurements \\
**Hidden acquisition cameras** & Available & Not available \\
**Performances for low-intensity AUs** & Poor performance, achieving recognition rate lower than 3D & Good performance for lower face AUs and low-intensity AUs \\
**Acquisition and recognition** & Frontal view recognition & Ear-to-ear frontal face acquisition \\
**Neutral scanning** & No need for neutral scanning & Often need for neutral scanning, a disadvantage for real-time applications \\
**Availability of databases with AUs** & High availability of databases, including public ones & Still low availability due to technical problems. Increase in recent years, thanks to the ease of 3D video capture \\
**Real-time** & Easy for a small amount of data & Not always good due to large time consumption \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Pros and cons of 2D and 3D methods. 2D methods. Based on the amount of data available, it is possible to decide whether to apply a deep learning technique to FER. Neural networks need a large amount of labeled data and considerable processing power, but allow one to perform feature extraction and their classification in an end-to-end way, reaching a state-of-the-art level of recognition accuracy. Alternatively, a conventional approach is recommended, and the most widespread is the feature-based algorithm, whereas fewer works focused on model-based or multimodal-based approaches. It is used both for the automatic AU recognition and for the basic emotions, obtaining better performances for the latter. The AUs are independent of the interpretation, and therefore more suitable to describe spontaneous facial behaviors. For this reason, thanks also to the technological development and the birth of the first databases that consider 3D dynamic and spontaneous facial expressions, in the future works we could expect a more in-depth study of the action units. Technological advances have allowed the development of research in 3D facial expression analysis. A large number of works still use databases for static 3D FER, most with posed and exaggerated expressions of the six basic emotions, in contrast to real life. The analysis of facial expression dynamics (4D FER) is increasingly expected shortly, along with the other significant challenge of recording spontaneous behavior, \"captured in a range of contexts having both high spatial and temporal resolution in real-time\" [9]. The next step will be to get closer to the real world. Nowadays, with the acquisition and processing tools available, accessible to all at affordable prices, it is easier to work with three-dimensional images and videos. The transition from 2D to 3D, although with some drawbacks above all related to dimensional and computational costs, is almost complete.