# Review: Facial Anthropometric, Landmark Extraction, and Nasal Reconstruction Technology
## 3 Facial Landmark Extraction
### Deep Convolutional Neural Networks
Previously, the Stacked Hourglass Network (SHN) was applied to detect 68 points on the face that was introduced by [PERSON] et al. [54]. The images were rotated and rescaled by using a Supervised Transformer Network, then four SHN was used to extract the features and locate the key points. To compare and evaluate the proposed deep model, the authors built two other models called LBF_Yang and SDM_Yang, and the accuracy of the proposed deep model was the highest of the three models, but the speed of the models was a few seconds per frame because the two stages consist of many parameters to be calculated. The paper of [PERSON] et al. [55] introduced Occlusion-adaptive Deep Networks (called ODN), which included three modules: geometry-aware module, distillation module, and low-rank learning module. The last layer of ResNet-18 was replaced by the occlusion-adaptive framework; their model is effective with occlusion problems. The feature map was extracted and fed into the geometry-aware module and distillation module, then the output of two modules was input into the low-rank learning module. Their model achieved a high accuracy for occlusion problems; however, the authors are not really satisfied with this result so, in a later study [56], they proposed to add the feature of attention to improve performance, namely, to replace the distillation module with the attentioned distillation module, and two factors, channel and spatial, were attentioned. Three models were introduced, including the geometry-aware module, the attentioned distillation module, and the low-rank learning module to achieve impressive results in occlusions and various poses. The aim of this study improved and developed previous research in terms of accuracy and speed. Another approach to increase model accuracy is to refine the loss function, this was presented in the research of [PERSON] et al. [57]. In their approach, they used ResNet, and they proposed a new loss function called Wingloss function. With this approach, their results proved the effectiveness of recommending a new loss function, and this function was tested on numerous architectures with impressive speed, namely, for a model with 3.8M parameters, the speed of the model was 2.5 ms per image, and for the Resnet model with 25M parameters, the speed was 33 ms per image; however, accuracy is also affected by different models. In another study, the authors used a set of anchor templates as references to address cases of the large variation in facial posture. A proposed model was introduced by [PERSON] et al. [58] called Anchorface; the model results were evaluated effectively on a 300W dataset. Overall, the studies surveyed show a trade-off between the accuracy and speed of the models. If many parameters are used to build a model, the speed is decreased, and vice versa. Therefore, novel algorithms have been proposed to have both the speed and accuracy of a specific model. For instance, [PERSON] et al. [59] used the MobileNet structure to speed up the model to avoid reducing the accuracy of extracted processing, and two different models were used--two teacher models and a student model. The structures in the training model stage were very complicated, but there are very few parameters while running the model. In their study, the loss functions were used differently in each stage. Assistive Loss (ALoss) was defined for the two teacher models, then the KD loss function was proposed to update the weight of the student model. According to the results of the article, the student model's parameters were lowered to the number of 2.4M parameters, namely, the MobileNet structure. This result was impressive, achieving quite a good accuracy with the occlusion cases, they called this model mnv2 KD. Moreover, the self-learning model is an algorithm that has recently attracted interest, being able to reduce the manual labeling process for training. In semi-supervised facial landmark detection, the study by [PERSON] et al.