# Face Recognition and Tracking Framework for Human-Robot Interaction
## 2 Related Works
### Facial Landmarks and Face Alignment Algorithms  
Face alignment plays a vital role in many computer vision applications. It is necessary to improve the robustness of face recognition against in-plane rotations and pose variations [20]. Meanwhile, facial landmarks are essential for most existing face alignment algorithms because they are involved in the similarity transformation for finding the closest shape of the face. So, facial landmark localization is a prerequisite for face alignment.  
Face alignment aims to identify the geometric structure of the detected face and calibrate it to the canonical pose, i.e., determining the location and shape of the face elements, such as the mouth, nose, eyes, and eyebrows.  
From an overall perspective, face alignment methods can be divided into model-based and regression-based methods [21]. However, the regression methods show superior accuracy, speed, and robustness when compared to model-based methods [22]. Furthermore, model-based methods show difficulties to express the very complex individual landmark appearance.  
[PERSON] et al. [23] further optimize regression-based methods by introducing a single convolutional recurrent neural network architecture that combines all stages' training through facilitating a memory unit that shares information across all levels. The importance of the initialization strategies for face alignment is demonstrated in [24]. Despite that, [PERSON] et al. [25] handled the sensitivity problem of initialization strategies by introducing the Deeply-initialized Coarse-to-Fine Ensemble (DCFE) approach. DCFE refines a CNN-based initialization stage with Ensemble of Regression Trees (ERT) to estimate probability maps of landmarks' locations. Cascade of experts is used by [PERSON] et al. in [26] to improve the face alignment accuracy versus the different face shape poses. [PERSON] et al. proposed Random Cascaded Regression Copres (R-CR-C) method that utilizes three parallel cascaded regressions. Furthermore, [PERSON] et al. [27] used a probabilistic approach to adopt coarse-to-fine shape searching.  
There have been significant improvements in face alignment using deep learning methods. As in [28], [PERSON] and [PERSON] introduced a single dendritic CNN, termed the Pose Conditioned Dendritic Convolution Neural Network (PCD-CNN). Furthermore, they combine a classification network with a second and modular classification network to predict landmark points accurately. In addition, [PERSON] et al. [28] proposed a boundary-aware face alignment algorithm that interpolates the geometric structure of a human face as boundary lines to improve landmark localization.  
In a later work, a more efficient compact model has been recently proposed by [PERSON] et al. named practical facial landmark detector (PFLD) [29]. They used a branch of the network to estimate the geometric information for each face sample to make the model more robust. PFLD achieved a size of 2.1 Mb and over 140 fps per face on a mobile phone with high accuracy against complex faces, including unconstrained poses, expressions, lighting, and occlusions, which makes it more suitable for HRI applications.