# Face Keypoint Detection Method Based on Blaze_ghost Network
## 1 Introduction  
Since AlexNet kickstarted the era of deep learning in 2012, using convolutional neural networks to detect facial features has become straightforward. Simultaneously, the mainstream approach to identifying a driver's fatigue state involves combining facial keypoint detection with facial fatigue evaluation indicators. However, recognizing facial fatigue indicators imposes strict requirements on the accuracy and precision of facial keypoint detection. Therefore, it is necessary to investigate existing facial keypoint detection algorithms and devise a high-precision and real-time keypoint detection model that accurately localizes keypoints to address the challenges in fatigue driving identification. Existing methods for facial keypoint detection can be classified into three categories: those based on ASM and AAM models, those based on cascaded shape regression models, and those based on deep learning.  
In 1995, [PERSON] et al. [1] proposed the Active Shape Model (ASM), which is a method for extracting keypoint coordinates on the distribution model of feature points (PDM). The overall process of detection involves the \"manual calibration of training sets-aligned shape model construction-search matching\". After improving the ASM model structure, the Active Appearance Model (AAM) [2] was proposed, which combines shape and texture information to locate keypoints based on the grayscale value of all pixels in the face. The ASM and AAM algorithms are pioneering algorithms in the field of facial keypoint detection, and many researchers have developed their own methods based on them [3, 4]. In 2010, [PERSON] [5] proposed the Cascaded Pose Regression (CPR) algorithm, which first sets the initial predicted value and then uses cascade regressors to further narrow the range of the initial prediction while gradually determining the shape of the object andfinally combines all regressors to obtain the final detection result. Afterward, many facial keypoint detection algorithms using feature detection combined with cascade regression appeared [6; 7]. In 2013, Professor [PERSON] [8] of the Chinese University of Hong Kong and his team proposed the first application of the deep convolutional neural network (DCNN) in detecting facial keypoints, which consists of three cascaded networks of different levels to detect the five keypoints of the left eye, right eye, nose, and mouth on both sides. The obtained keypoint coordinates are more accurate compared to the first two methods. [PERSON] et al. [9] used a 68-point dataset for training the cascade network and proposed an improved DCNN model. Since then, cascade regression convolutional neural networks have become the mainstream method for researching facial keypoint detection, including many algorithms, such as the TCDCN [10] network and MTCNN [11] network proposed by [PERSON] et al. and the PFLD algorithm proposed by [PERSON] and others [12].  
In summary, the current facial keypoint algorithms have poor generalization in complex environments, and the balance between accuracy and speed needs improvement. Therefore, this study aims to address these issues and enhance the robustness and performance of facial keypoint detection algorithms. Specifically, based on the PFLD facial keypoint algorithm, this paper integrates BlazeNet [13] and Ghost Module [14] models to design a new backbone network called Blaze_ghost. ASPP (Atrous Spatial Pyramid Pooling) is also incorporated for more accurate keypoint extraction. Additionally, a new loss function called HuberWingloss is introduced, and the Adadeleta [15] optimizer is used. These improvements enable the enhanced facial keypoint model to extract keypoints more accurately in a shorter time, meeting the requirements for fatigue feature extraction in driving environments.