# Augmented EMTCNN: A Fast and Accurate Facial Landmark Detection Network +
## 2 Related Works  
Traditional real-time extraction methods in the fields of human images have been proposed in many research studies [18; 19; 20; 21]. However, these conventional approaches have the drawback of using hand-crafted features. Recently, CNNs were demonstrated to be superior in the diverse fields of computer vision, energy forecasting [22; 23], and biomedical science [24; 25; 26; 27; 28]. This is because it performs overwhelmingly well in feature map extraction from the convolution layers. Various studies on the extraction of high-quality feature maps based on the CNN architecture are underway [14; 15; 16; 17]. The quality of facial landmark extraction has also been improved remarkably by using CNNs. However, in the case of high-resolution images, performing facial landmark extraction on the entire image requires a large amount of computation, and the results could be inaccurate. To alleviate such problems, the landmark extraction process can be divided into two stages: detecting facial regions and extracting facial landmarks in those detected regions. A facial region in an image can be detected by using an object recognition technique, and many object detection algorithms have been proposed to localize objects within the image. For instance, Region CNN (R-CNN) [29], which is the first CNN-based object recognition model, uses the selective search algorithm [30] and creates bounding boxes by combining pixels that have similar feature patterns, then classifies images using CNN and support vector machine (SVM) [31]. However, this method requires a considerable amount of processing time because all the bounding boxes are fed to the CNN as input. To solve this problem, [PERSON] [32] proposed the fast R-CNN based on the concept of region of interest (ROI) pooling. Instead of feeding all the bounding boxes to the CNN, they pooled the regions corresponding to ROIs from the final feature map of the CNN. Both R-CNN and fast R-CNN use the selective search algorithm to detect bounding box candidates. However, as selective search is a slow and time-consuming process, it affects the performance of the network. To rectify this, [PERSON] et al. [33] proposed faster R-CNN to model ROI proposals of the region proposal network (RPN). They used a sliding window to compute the coordinates and scores of the bounding boxes. On the other hand, [PERSON] [34] proposed You Only Look Once (YOLO), which divides the image into N \(\times\) N grids and computes the class probability and offset value along with the bounding boxes on the grid. Then, they selected the bounding boxes that had a higher class probability than some threshold value. Even though YOLO could detect object regions very quickly compared to other models, it had the problem of low accuracy. To improve the detection accuracy, [PERSON] et al. [35] added a batch normalization process to all the convolution layers, replaced the fully connected layer with a convolution layer, and extended the final feature map size from \(7\times 7\) to \(13\times 13\). Through these attempts, they detected 9000 object classes while further improving the speed and performance, referred to as YOLO9000. In addition, they recently reported a new model that improves the performance further by introducing a new network [36]. As mentioned earlier, facial landmark extraction can be done in conjunction with face detection to reduce computation time and improve extraction accuracy. One popular approach for facial landmark detection is to use an open library such as Dlib [37]. This library has shown reasonable performance in face landmark recognition using the ensemble approach of regression trees proposed by [PERSON] and [PERSON] [38]. Another approach for facial landmark detection is to use CNN-based algorithms. [PERSON] et al. [39] first suggested a method of using cascaded CNNs to extract facial landmarks. They obtained facial landmark points from the first network and then refined the results through shallow networks.