# Multi-Layer Progressive Face Alignment by Integrating Global Match and Local Refinement
## 1 Introduction
### The Motivation of Our Method  
Many researchers have considered combining the two methods in different manners to achieve accurate and stable performance. A straight-forward idea is to estimate the initial positions of the whole face and refine the components locally and independently [37; 38]. The local features are used to train a series of random forests for each landmark independently; then, the global features are used to train a random fern to constrain the global shape [39]. The global Net is used to roughly estimate the pose and select the initial shape; then, the local Net is used to predict the shape and pose residuals [40]. The local misalignments are restricted by holistic appearance fitting and the global misalignments are avoided by restricting the local movements [41; 42; 43]. [PERSON] et al. [44] predict landmarks by CNN to obtain rough positions and locally refine different parts by several small regional CNNs. [PERSON] et al. [45] use two-stage re-initialization with deep network regressors, in which a coarse face shape is predicted in the global stage and each landmark is estimated respectively in a local stage. From the view of multi-source information fusion, integrating data from different channels and combining different processing methods can obtain better performance for a specific task. Widely studied emotion recognition investigates multimodal data, including visual, audial, physiological signal, text, and so on. [PERSON] et al. [46] integrate these direct and relative prediction perspectives to obtain excellent performance on an audio-visual emotion recognition benchmark. [PERSON] et al. [47] utilize a CNN to extract robust features from speech, and a deep residual network (ResNet) for the visual modality. [PERSON] et al. [48] demonstrate that the Bimodal Deep AutoEncoder (BDAE) features are effective for emotion recognition. By analyzing the confusing matrices, EEG and eye features can contain complementary information. [PERSON] et al. [49] propose a novel multimodal emotion recognition system based on visual and auditory information to estimate different emotional states in real affective human robot communication. [PERSON] et al. [50] applied four deep belief network (DBN) models to generate robust multimodal features for emotion classification. Then, convolutional deep belief network (CDBN) models are proposed to learn salient multimodal features of emotions expressions. The current methods mostly do not jointly consider appearance representation and the alignment strategy. Furthermore, the appropriate features and approaches for the different alignment stages are not carefully selected. The different features from a whole face and a certain region should be respectively extracted based on the different characteristics of the alignment stage. The different scale image information should be combined in different manners as a more descriptive face representation. The current methods mostly reduce the influence of initialization errors by setting multiple initial positions or by direct regression from the face texture. Variations in initial shapes satisfy a certain kind of transformation. The transformation from mean shape to ground-truth should be investigated. The current methods seldom combine traditional handcrafted features with the popular CNN features. Handcrafted features contain more prior knowledge,which helps to represent a face. Accordingly, handcrafted features should be introduced into popular CNN networks as to achieve better performance. Our research is motivated by the belief that more competitive face alignment results can be obtained if we combine representation information with different scales and apply different methods and approaches for alignment tasks in different phases. Global alignment is more suitable for representing the holistic structure, and therefore, can achieve the global optimum in matching a whole face. Local alignment is good at describing shape details; therefore, it is suitable for refined corrections of given area.