# Review: Facial Anthropometric, Landmark Extraction, and Nasal Reconstruction Technology
## 1 Introduction  
Anthropometric data is the measurements of human size and shape, which are usually used to reconstruct a digital human morphology. The figures are usually used to identify the gender, ethnicity, or age of humans in different regions; moreover, they are also used in clinical diagnosis or as a reference for patients before and after surgery. Moreover, they are also used as reference parameters in the design of personal protective equipment. Reconstructive surgery is repairing congenital defects, damaged organs, or parts of the body destroyed after a trauma. This helps patients feel more confident in communication or is used in cosmetic surgery to increase facial beauty. In cosmetic surgery, doctors try to create 3D models that are used as a supported tool to determine clinical diagnoses and communicate with patients. The stages of building 3D images are created thanks to the techniques of the staff, which increases their work pressure. The support of machine learning algorithms needs to be applied to automate these processes; recently, the algorithms for identification, classification, or recognition have been introduced in many different fields, such as agriculture, medicine, transportation, etc. [1; 2; 3], to improve the quality of human life. Artificial intelligence (AI) was first introduced in 1956 at Dartmouth College. It is a term for machine learning intelligence through learned weights, it is used in research about nature language networks and classifiers to solve real-world problems. Facial landmark(aka keypoint) detection is a necessary and first task in facial human studies [4]. Further, the information on the facial landmarks is also used in areas such as human-computer interaction, face recognition, and emotion recognition [5; 6; 7], and, in particular, it is applied in anatomical problems and reconstructing 3D images for clinical diagnosis [8]. The number of facial landmarks is determined differently based on its application. For example, [PERSON] et al. [9] detect fatigue based on 68 facial landmark extraction, [PERSON]. [10] identify 66 landmarks to recognize facial emotions, [PERSON] et al. [11] determine 19 landmarks for face detection, etc., which is presented in Section 3. In medical and cosmetic surgery, simulating procedures is necessary for clinical diagnosis, patient education, and medical education; however, the research on reconstructing parts of the human body is a significant challenge. Patient education impacts how patients behave and results in the influence of knowledge, attitudes, and skills required to preserve or enhance health [12]. This means that doctors can teach patients about their faces before and after surgery based on simulation models. The facial landmarks are the key points determined on a human face, which are specific, such as the eyes, nose, mouth, etc. [13]; moreover, they are used to calculate the anthropometric measurement. The difficulty of facial landmark detection is due to the location of the landmark points, which change in various poses combined with the fact that the face surface is not flat, thus, each point is captured by different light at different locations on the face, all of which creates noise that interferes with recognition. Facial landmarks are inputs of the different studies, such as face detection, doze detection, etc. In many different countries and regions, three-dimensional image analysis is a topic that has attracted interest and prompted the creation of anthropometric databases for different purposes; this is presented in Section 2. Doctors can rely on anthropometric data to make clinical decisions before surgery, however, this depends heavily on their experience. With the development of technology, many algorithms were proposed to recognize landmarks automatically, such as Active Appearance Models (AAM), Support Vector Machine (SVM), Regression Trees, Coarse-to-Fine Shape Searching (CFSS) [14], Fast Shape Searching Face Alignment (F-SSFA) [15], etc., or the deep convolutional neural network algorithm, which is in Section 3.