# Review: Facial Anthropometric, Landmark Extraction, and Nasal Reconstruction Technology
## 3 Facial Landmark Extraction
### Non-Deep Convolutional Neral Networks  
Besides using DCNN, some proposed models, such as the Active Shape Model, Active Appearance Model (AAM), Support Vector Machine (SVM), coarse-to-fine framework, etc. [62, 63, 64, 65], are non-deep models that are often used in landmark detection. The advantages of these methods over the DCNN approach are that the number of parameters is less, and the model structure is simpler, however, they often handle tasks with direct computations. For example, the Support Vector Machine (SVM) classifier was used to calculate the score of each point on the image that was introduced by [PERSON] et al. [15]. They built a global detector to handle the case that goes astray from the local detector and their innovation in the study was to combine the Bayesian model with the local detector and nonparametric global models. The local detector process takes up a lot of time because it has to slide and calculate many scores for each pixel, thus, the speed of their model was lower than the current approach, and the time to process an image was 400 ms. Face alignment was introduced by [PERSON] et al. [66], a new method based on regression trees, the loss functions were optimized to minimize at test time; similarly, [PERSON] et al. [67] applied the Cascaded Regression tree model to detect the key points. Unlike the different approaches, the idea of finding a preliminary assessment of the locations of landmarks and then using the added model to refine the facial landmark locations was presented by [PERSON] et al. [14]. To reduce the dependency on the initialization of the model cascades, they proposed a coarse-to-fine framework that could consider and solve the diverse pose. Their result shows that this method was more effective than the cascade model for significant changes in pose. However, the number of computations of this algorithm is enormous compared to its real-time applications, which are difficult. To solve this problem, another study [64] proposed the F-SSFA model to improve the speed of their model. Their result shows that the processing speed is impressive, reaching 1.43 ms per image, 53 times more than the previously proposed CFFS model. This approach was quite similar to the approach of [PERSON] et al. [65] about how the model works, however, the authors used different architectures to solve their problems. Coarse-to-Fine Auto-encoder Networks were proposed to locate the landmarks, the points were identified and preliminarily evaluated before being refined by the 2 nd Stacked Auto-encoder Networks (called SANs) with high accuracy. The structure of their model was built with the desire to reduce calculations compared to other models, the low-resolution image was used to estimate landmark locations, then the model was used to refine the coordinate positions of facial landmarks.  
Their result showed that the speed of the model archived 23 ms per image to detect 68 facial points. In a previous study, a Support Vector Machine (SVM) was used as a local detector to return a score at the focal point, then the global model was applied by using the Bayesian model that was presented by [PERSON] et al. [15]. Their model was the non-parametric model, and the responses of the key points are considered as a hidden variable, and a consensus of exemplars was used to optimize this global model. However, the localizer takes 400 ms per fiducial, and the local detector takes up most of the model time.  
The studies of facial landmark extraction are summarized in Table 2, including using the deep CNN model and non-deep CNN model. The factors are considered, such as the number of landmarks, year, and model used that was introduced in each study. Nevertheless, fps is for reference only because it depends on many factors, including the hardware of each study being different.