# Review: Facial Anthropometric, Landmark Extraction, and Nasal Reconstruction Technology
## 3 Facial Landmark Extraction
### Facial Landmark Detection in Medicine
The purpose of this study was to use 4 out of 16 measurements to predict age group by using SVM-Sequential Minimal Optimization (SMO) with an accuracy of 96%. With the same method and purpose, [PERSON] et al. [72] with 11 horizontal and 5 horizontal measurements were used to predict age group by the SVM-SMO algorithm. Recently, a facial anthropometric collection system for a Vietnamese population was proposed by [PERSON] et al. [73], and YOLOv4 was applied to detect anthropometric features with 27 anthropometric points. In their other study of the same data set, they proposed Faster R-CNN [74] to detect 14 key points on the human face, with the aim of automatically identifying landmarks, then the 3D model was built for medical applications and pre-diagnosis for cosmetic surgery. They also recorded anthropometric data of 182 Vietnamese aged from 23-46 years old, this is also the anthropometric dataset collected on the Vietnamese. [PERSON] et al. [75] made an assessment of facial palsy using a machine learning model previously exported to identify 68 landmarks; they hypothesized that previous studies using training datasets collected from normal people had not been demonstrated to be sufficiently accurate when tested in patients. The authors trained and compared the accuracy of the machine learning model on two data sets, one is the available data called 300-W and the data set they collected on the patients; the results were very impressive. The purpose of the study was to identify landmarks in facial paralysis patients, thereby objectively assessing the patient's disease, and the hypothesis about their training dataset was demonstrated. In the study of [PERSON] et al. [76], the authors proposed to detect acromegaly from human face images by machine learning methods. The state-of-the-art models were applied in this study, such as Generalized Linear Models (LM), K-nearest neighbors (KNN), Support Vector Machines (SVM), Forests of Randomized Trees (RT), Convolutional Neural Network (CNN), and Ensemble Method (EM), which increased the accuracy of acromegaly detection, however, multiple models were used to increase the model's calculation parameters. The facial landmarks were identified through three main steps: recognize faces from images using the SVM model, then extract features of the image, namely, landmarks by CNN, and classify as acromegaly or not. The structure of this method was very complicated, making it difficult for real-time applications, yet, in medicine, accuracy is always the most important factor. Twenty-seven key points on the human face were recognized by using the VGG16 model, researched by [PERSON] et al. [77]. The purpose of their study was to predict congenital adrenal hyperplasia from the analysis of facial landmarks. The limitation of their study is that the dataset size was limited, and the predictions made only on 2D images. Their results showed that there was a difference in the face of normal people and patients, which had great significance in the field of medicine. Another application of AI called DeepGestalt, was proposed by [PERSON] et al. [78]. They studied genetic diseases using anthropometric landmarks on the patients' faces, they identified 130 landmarks on the patients' faces by using ten convolutional layers. DCNN cascade was applied to detect the face of the patients, then the landmarks were detected, and the accuracy of prediction achieved was 91%. This is highly regarded research and opens up a lot of potential in the field of medicine. Furthermore, Williams-Beuren syndrome was researched by [PERSON] et al. [79], who used five CNN architectures to identify five landmarks for WBS facial identification in clinical practice. They collected data to train a model of 340 participants, and train on different models, from their published results, the VGG19 model is rated as the model with the best performance, however, its number of parameters is quite large (144 million) compared to other network architectures, such as ResNet-18, ResNet-34, and MobileNet-V2.