# 3D Approaches and Challenges in Facial Expression Recognition Algorithms--A Literature Review
## 3 Conventional and Deep Learning-Based Approaches
### Feature-Based VS Model-Based Algorithms  
Existing conventional approaches for FER based on 3D static data can be categorized into feature-based algorithms and model-based ones. The first category focuses on the extraction, directly from the input data, of facial surface geometric information such as curvature, spatial relations between pairs of interest landmarks (Euclidean and Geodetic distances), gradient and local shape. Features are usually calculated on the region surrounding principal facial landmarks or on the mouth and eyes that inherently contain essential information for emotion recognition. These key features that are considered closely related to expression categories, in order to perform FER are fed to various classifiers, as well as Support-Vector Machines (SVM) [47; 48; 49; 50; 51], Adaboost, k-Nearest Neighbors (k-NN), Linear Discriminant Analysis (LDA), Modified Principal Component Analysis (PCA), Hidden Markov Model (HMM) [44; 45; 46], Random Forest [52] or Neural Networks [51; 53; 54]. Feature-based methods are straightforward, but present two main drawbacks. First of all, most of these kinds of approach need a set of correctly located landmarks for feature extraction, an additional step of the process. If until a few years ago it could be considered a difficult task in real-world applications because it required the manual localization of landmarks for feature localization or surface registration, to date this step has been automated, as recently reported by [PERSON] [55] and [PERSON] [56]. Moreover, the performance of the model is directly related to the discriminative power of facial features adopted instead of shapes. Most of the works found in literature make use of the landmarks manually labeled on the BU-3 DFE or Bosphorus dataset. Experiments have proven their excellent performance in recognizing universal expressions, but most of the features used have not yet shown enough discriminative power for distinguishing subtle facial AUs. Thanks to the technological development in imaging and scanning, nowadays it is possible to capture 3D scans and extract geometric characteristics from certain regions around facial landmarks. Examples of some popular expression features are the distances between 3D facial landmarks [57; 58; 59; 24], 3D facial curves [60], distances between locally extracted surface patches [56], facial geometry images and normal maps [61]. Another possible technique exploits 3D face descriptors, which derive from depth maps by using mathematical operators: first principal curvature, shape index, mean curvature, curvedness, etc. Descriptors are presented and fully explained in [62; 63]. Table 2 gives a comparison of selected 3D FER algorithms that use facial features to perform expressions recognition. The works are listed chronologically by year of publication, and alphabetically by author. The model-based methods, on the other hand, make use of a generic face model created using the neutral expression to determine emotions by measuring the feature vector formed by the coefficient of shape deformation. This approach needs to bring into correspondence the tracking model to 3D face scans by means of a registration step. [PERSON] et al. [30] presented an automatic 3D FER approach to perform expression prediction by combining a Bayesian Belief Net (BBN) and Statistical Facial Feature Models (SFAM). They tested the developed automatic method on the BU-3 DFE database, applying the SFAM for the automatic recognition of the six universal expressions reaching an average recognition rate of over 82%. In [64], [PERSON] et al. proposed a real-time 3D model-based emotion recognition method. The 3D model of the face, which gives essential clues for improving sturdiness and allows managing large head rotations, rapid head movements, and partial facial occlusions, is restored by 2D images. More recently, [PERSON] et al.