Accurate facial paralysis degree evaluation method and device based on H-B grading under CV
An evaluation method and technology of facial paralysis, applied in neural learning methods, acquisition/recognition of facial features, instruments, etc., can solve problems such as large errors and low evaluation efficiency
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Embodiment 1
[0082] See figure 1 This embodiment provides an accurate facial paralysis evaluation method based on HB grading under CV. This method can be applied to facial paralysis detection equipment, as a detection method of medical equipment to detect the degree of facial paralysis of patients with facial paralysis, and can be large-scale, It can be widely used in industrial applications, for example, it can be used as an independent program in mobile phones and clients, which can be used for correction and inspection of facial paralysis patients during non-treatment periods, and it can also be used as a preventive method for non-facial paralysis patients. The method for evaluating the degree of accurate facial paralysis includes the following steps, namely steps (1)-(3).
[0083] Step (1): Establish a key point detection model for facial paralysis. In this embodiment, the method for establishing the key point detection model of facial paralysis includes the following steps, namely steps...
Embodiment 2
[0118] See image 3 This embodiment provides an accurate facial paralysis evaluation method based on H-B classification under CV. The method is similar to that of Embodiment 1, except that the deep full convolutional network model of this embodiment is different. The specific structure of the deep fully convolutional network model of this embodiment can be designed separately according to the specific requirements of users. For the convenience of further introduction, an example of the deep fully convolutional network model structure is now designed as image 3 Shown. The number of down-sampling and up-sampling layers of the deep full convolutional network model is 3 layers, and the down-sampling adopts maxpooling maximum pooling method. The pooling layer size is 2×2 and the step size is both 2. In the dconv deconvolution method, the size of the deconvolution layer is 2×2 and the step length is both 2, and each adjacent up-sampling or down-sampling is separated from the convolut...
Embodiment 3
[0120] This embodiment provides an accurate facial paralysis degree evaluation device based on H-B grading under CV. The device applies the accurate facial paralysis degree evaluation method based on H-B grading under CV in Example 1 or Example 2. Among them, the accurate facial paralysis degree evaluation device includes a detection model establishment module, a data acquisition module, a data processing module, and a facial paralysis degree comprehensive evaluation module. The data acquisition module and the data processing module can form a data acquisition and processing module to be detected. These modules can be used as computer program modules or as hardware modules, which can execute the relevant steps introduced in Embodiment 1 or Embodiment 2.
[0121] The detection model establishment module is used to establish a facial paralysis key point detection model, which is actually used to perform step (1) in Embodiment 1. In the facial paralysis key point detection model, de...
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