# Review: Facial Anthropometric, Landmark Extraction, and Nasal Reconstruction Technology
## 3 Facial Landmark Extraction
### Deep Convolutional Neural Networks
With this approach, the number of parameters is quite large, so processing speed is not the main factor of interest in this study, and it is difficult for real-time applications. In the research by [PERSON] et al. [47], they proposed the Stacked Hourglass Network to predict the location of the facial landmark point. The Residual Network (Resnet) was applied as the backbone instead of the \(7\times 7\) convolution layer, furthermore, the \(1\times 1\) convolution is used to reduce the parameters of the model. To improve the accuracy of their model, Original Residual blocks were proposed instead of the Residual Dense blocks, but the speed of the model is lower. According to the authors, their proposed model takes 60 ms for landmark detection, which is approximately 12 times slower than the 3 DDFA [48] method but the accuracy is higher. Therefore, other networks with fewer parameters such as MobileNet can be considered to replace ResNet. Moreover, the authors had not solved the problem of occlusion; the results show that with the occluded cases, the model's predictions became mixed up. A study by [PERSON] et al. [48] proposed a combination of three models consisting of 3 DMM, Cascaded Regression, and CNN to align the face when changing pose. 3D Dense Face Alignment (3 DDFA) was proposed in their work, and a cost function was also introduced to increase the speed of the model. According to the authors, traditional models are not flexible enough to recognize points when appearance changes or major changes in pose. In their work, they reconstruct the face from the 2D image and the entire process takes 63.9 ms. To optimize the model parameters, the authors propose a novel cost function named Optimized Weighted Parameter Distance Cost (OWPDC) with demonstrated efficiency compared to different cost functions. In a different approach, the proposed network structures are used and calibrated to suit the intended application, namely using YOLO networks in the study of [PERSON] et al. [49]. They used YOLO to detect the face, then the Active Shape Model (ASM) was used to extract the landmark points. Previously, the HAAR cascade classifier was applied to detect two key points on the human face that was presented by [PERSON] et al. [4]. Their applied YOLO model gave surprising results in terms of model accuracy and speed. Their result showed that landmark detection takes 0.0021 ms per image, and the time of the ASMmodel to detect landmark points was not presented by the authors. In their study, the YOLO model was presented carefully, but it was only used for face detection, and the facial landmark detection was carried out by using the ASM model. The input of this model is a 2D image with a frontal face and it does not deal with occlusion cases. In addition, the evaluations are only performed on a small sample, which greatly affects the strength of the conclusions about the accuracy of the model. Another approach many authors have used to achieve the advantages of different machine learning models is to introduce hybrid models, which means combining the CNN model with a different model as a classifier layer of the traditional CNN. CNN was used to extract the features, then the Support Vector Machine (SVM) was applied to recognize facial landmark points that were researched by [PERSON] et al. [50]. The classifier of the traditional CNN is usually dense layers with softmax function, and these layers consist of many parameters that reduce the speed of the model. Similarly, [PERSON] et al. [51] also used CNN to extract features of the image, then Conditional Random Field was used in addition rather than just fully connected layers. Furthermore, the CNN-LSTM-RNN model was proposed to detect the facial landmarks by [PERSON] et al. [52], Long Short Term Memory (LSTM) was used to find the initial location, then they applied a model with the same structure as suggested by [PERSON] et al. [53] to refine the results. The result was a high evaluation and the accuracy of their proposed model was impressive, which is shown in Section 4.