## 2 Related Work  
### Features for Facial Expression Recognition  
Facial expression analysis usually employs feature extraction methods to describe images in different ways and to facilitate system classification. In the current practice of facial expression recognition analysis, there are many typical feature descriptors, where different extracting strategies have different principles. According to our understanding, there are three categories. One category is texture-based feature. In this category, one of the common features is a global one, using transforming domain methods, such as Gabor wavelet and Principal Component Analysis (PCA), to extract facial appearance information [2; 3; 4; 5; 6]. In the last decades, the most efficient facial description method utilizes histograms to record feature vectors such as Histograms of Oriented Gradients (HOG) [7; 8], Histograms of Local Binary Patterns (HLBP) [9; 10; 11] and Weber Local Descriptor (WLD) [12; 13], which calculated the change in orientation or a stimulus (such as sound, lighting) with facial expression. The other popular feature extraction methods include Haar-features [14; 15] and scale-invariant feature transform(SIFT) descriptors [16]. One category is a geometric feature derived from landmark extraction. This is normally based on an established two dimensional or three dimensional (2D/3D) facial model to locate feature points based on shape parameters and various derived facial detection models, such as Active Appearance Mode (AAM) [17; 18]. Some fast landmark extraction methods have been proposed in recent years by using the combining method with face and eye detection with the Vilas-Jones algorithm and together with a local descriptor to detect the interested region and extract the extreme points as landmarks [19]. Other research extracts the accurate localization of landmarks, relying on the geometrical properties of facial shape with the differential geometry as the theoretical substratum [20]. The derived geometric feature could be simple, with geometric features such as lip width, and with lengths describing the expression states such as how open the mouth is. Some research derived more complex geometric features, for example, calculating a set of geodesic and Euclidean distances, together with nose volume and ratios between geodesic and Euclidean distances; specifically, on the mouth and eyebrows as a separate study for facial expression analysis in paper [21]. One category is temporal feature, calculated from the image sequence, which has the benefit of revealing the dynamic attribution of expression and improves the discriminative performance in expression recognition. Optical flow is one popular and traditional dynamic information extraction method for generating features [16; 22]. Face tracking is another common approach to locate and track the landmarks and derive the temporal features. As demonstrated in the research by [PERSON] et al., they use Elastic Bunch Graph together with eye detection to establish the landmarks, then the tracking is done among two adjacent frames by iteratively processing and calculating displacements with Gabor jet. The feature is generated with a derived geometric representation of a single frame image and a set of the transformed landmark coordinate positions of succeeding frame images. It needs to be mentioned here that the ensemble classification scheme achieves high recognition accuracy using multi-class AdaBoost and support vector machines, respectively [23]. With development of the deep learning algorithm, the learning based feature extraction, instead of the above manual construction ones, is applied in the facial expression recognition fields [24]. However, the requirement of the large training sample of deep learning based feature extraction is not practical in most facial expression applications, with the obstacle of capturing and labelling enough expression samples. Furthermore, the traditional facial expression feature becomes mature after decades of research.