True and false facial paralysis identification system based on depth difference feature

A recognition system and differentiated technology, applied in the fields of medical treatment and image recognition, can solve the problems of misjudgment of facial paralysis recognition method, deviation of recognition result accuracy, and reduction of facial paralysis recognition accuracy, so as to improve the recognition rate, avoid feature extraction, and strengthen the recognition of facial paralysis. The effects of self-stability and individual differences

Active Publication Date: 2019-03-29
谢飞
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AI Technical Summary

Problems solved by technology

Because the researchers ignored the existence of false facial paralysis, the existing facial paralysis recognition methods have misjudgments, and the

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  • True and false facial paralysis identification system based on depth difference feature
  • True and false facial paralysis identification system based on depth difference feature
  • True and false facial paralysis identification system based on depth difference feature

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Embodiment Construction

[0043] By analyzing the images and video data of real and fake facial paralysis, it is found that when patients with facial paralysis repeat an action (such as shrugging the nose, showing teeth, bulging the cheeks, closing eyes, etc.), the patient does not make any obvious movements almost every time. However, for subjects with fake facial paralysis, if they repeat the same fake facial paralysis action at different times (normal people imitate the actions of patients with facial paralysis), there will often be obvious differences before and after the action, such as figure 1 shown.

[0044] According to the above situation, we believe that an important basis for identifying true and false facial paralysis is the difference between the front and rear movements at different moments. When the difference between the front and back movements is large, there is a greater probability that it is false facial paralysis. When the difference between the front and rear movements is small, ...

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Abstract

The invention discloses a true and false facial paralysis identification system based on depth difference features. The system comprises a training image acquisition module, which is used for acquiring training images and establishing a training image set; an identification network establishing module, configured to establish an identification network, and train the identification network by usingthe training image set to obtain an identification model; The identification network extracts the difference information of the deep features of the input image through the dual-branch convolution neural network, and then extracts the depth features by using the difference information of the single-branch convolution neural network to identify the true or false facial paralysis according to the difference of the depth features. The recognition module is used for acquiring an image to be recognized, and performing recognition to obtain a recognition result. The invention has good recognition effect, can effectively complete the recognition of true and false facial paralysis, and has important practical application value in clinical diagnosis.

Description

technical field [0001] The invention relates to the technical fields of medical treatment and image recognition, in particular to a real and fake facial paralysis recognition system based on depth difference features. Background technique [0002] Facial paralysis is a common disease with a wide range of incidence and is not limited by age. It will not only affect the life of the patient to a certain extent, but also cause a certain blow to his heart, seriously affecting the physical and mental health of the patient. With the increasing incidence of facial paralysis, more and more scholars have begun to pay attention to the identification of facial paralysis. [0003] In order to realize the automatic identification of facial paralysis, many scholars at home and abroad have conducted research on this aspect. They focus on static facial asymmetry and dynamic facial changes, track the movement differences of key points, and use deep learning methods to locate key points. Meth...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04
CPCG06V40/161G06V40/168G06V40/172G06N3/045
Inventor 谢飞
Owner 谢飞
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