# Review: Facial Anthropometric, Landmark Extraction, and Nasal Reconstruction Technology
## 3 Facial Landmark Extraction
### Deep Convolutional Neural Networks  
In this section, some recent studies on facial landmark extraction are reviewed. The purpose of this section is to provide a review of the published studies in this field using DCNNs, but the scope of these studies is not based on theories in cosmetic surgery. They are used in information technology applications, particularly facial analysis. Of course, facial landmark detection is the first step in the applications of the human face or facial attribute analysis. The number of points can differ based on the applications, for example, [PERSON] et al. [13] detected five key points on the face by using a convolutional neural network that has a structure of six layers, including the input of the model that is the extracted face image, three convolution layers, and two fully connected layers. However, the performance is not clearly presented, it is only shown by some experimental images, and the accuracy of the model is not optimal, especially the large mean error for occlusion. For the purpose of reducing occlusion errors, a study by [PERSON] et al. [42] proposed a different traditional networks model, called Deep Convolutional Network Cascade, which is divided into three levels. The model has a complex structure, so the speed of the model is not optimized, each image takes 120 ms to extract five points with higher accuracy. The limitation of their study was that it did not detect a large number of landmarks and the speed of the model was very low. However, at this time, the old equipment did not meet the processing speed of  
Figure 1: Depicting the location of landmarks on the human face presented in the reviewed studies based on Farkas Atlases [30; 34]. the AI models, hence, these studies are highly appreciated and are a premise for further research. Afterward, a novel end-to-end CNN model was introduced by [PERSON] et al. [43], their architecture consists of shared layers and component-aware branches. The first part extracts the features by using two convolutional layers, and the second part includes six branches to reduce imbalanced errors between parts of the human face. A later study by this same group of authors suggested using a combination of Branched Convolutional Neural Networks (BCNNs) with Jacobian Deep Regression (JDR) to boost the accuracy of landmark locations [44]. By using an additional JDR model, the coordinates of landmarks are refined, increasing model accuracy and managing uncontrolled conditions. Of course, the speed of the model is significantly reduced, an image takes approximately 25 ms, whereas the BCNN model takes 12 ms; this means that by using the BCNN-JDR combination, the time to recognize an image is doubled compared to previous research [43]. The accuracy is affected by occlusion cases, which is a big issue in facial landmark location, with possible reasons being the angle of the face, the pose, light, and defects, thus, there are many studies are carried out to solve this problem. For instance, the occluded locations are found using the locations of nearby visible points, which is proposed by [PERSON] et al. [45]. Two types of loss functions are applied to find the matching heatmap and manage missed points. In their study, Cascaded Heatmaps Regression into 2D Coordinates (CHR2C) appended with two encoder-decoder CNNs similar in structure was introduced to estimate landmark points in different poses and occlusion. However, the model was not accurate for large occlusions and makeup faces. The processing speed of the model is rated at an average of 90 ms per image, but according to the authors this speed can be improved by removing part of the proposed model. A new location is determined thanks, in part, to the location of previous points that was also proposed by [PERSON] et al. [46]. In their study, a new approach was presented, namely the VGG19 network, applied to estimate the initial coordinates, then the LSTM model was used to refine the facial landmark detection.