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Skin problem diagnosis method based on deep learning face partitioning

A deep learning and problem diagnosis technology, applied in the field of image processing, can solve problems such as partition standards that are not described in detail, and achieve the effect of good applicability and real-time performance.

Active Publication Date: 2019-11-19
广州纳丽生物科技有限公司
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Problems solved by technology

This method mentions a face partition method, but it is mainly used for face recognition, and does not describe the partition standard in detail

Method used

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  • Skin problem diagnosis method based on deep learning face partitioning
  • Skin problem diagnosis method based on deep learning face partitioning
  • Skin problem diagnosis method based on deep learning face partitioning

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Embodiment Construction

[0019] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the embodiments and accompanying drawings.

[0020] Such as figure 1 As shown, the workflow of the skin problem diagnosis method based on deep learning face segmentation includes the following steps:

[0021] Step 10 Collect multiple face images, according to the forehead area A H , left orbital area A EL , right orbital area A ER , nose bridge area A N , left cheek area A CL , right cheek area A CR , Chin area A J 7 partitions mark faces to form a face partition labeling dataset;

[0022] Step 20 Train the deep learning instance segmentation model on the face partition labeling dataset, so that the partition classification cross entropy L CrossEntropy , The positioning accuracy function L of the outer frame of the partition Detect , Partition pixel classification accuracy L Mask Th...

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Abstract

The invention discloses a skin problem diagnosis method based on deep learning face partitioning. The method comprises the steps that multiple face images are collected, faces are marked according toseven partitions including a forehead area AH, a left orbital area AEL, a right orbital area AER, a nose bridge area AN, a left cheek area ACL, a right cheek area ACR and a jaw area AJ, and a face partition marking data set is formed; a deep learning instance segmentation model is trained on the human face partition annotation data set to enable three deviation functions, namely partition classification cross entropy LCrossEntropy, partition outer frame positioning accuracy function LDetect and partition pixel classification accuracy LMask, to have minimum values; a face region is segmented byusing the trained instance segmentation model, and that a skin problem exists in each partition is determined; a skin problem is diagnosed according to the regional priori knowledge, and a corresponding treatment scheme is given. According to the method, the skin problem diagnosis for human face partitioning is realized, the applicability is good, and an intelligent skin problem partitioning classification diagnosis and treatment scheme is provided on the premise of ensuring the real-time performance.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image processing method for skin problem diagnosis based on deep learning face segmentation. Background technique [0002] With the continuous development of the beauty and skin care industry, people's pursuit of health and beauty is gradually increasing. Skin problems in various areas of the human face correspond to different health problems. Therefore, how to provide skin problem diagnosis for different face partitions has become some key technologies. . At present, the domestic face and skin partition diagnosis technology is not mature, mainly relying on the subjective judgment of doctors, there is no uniform standard for partition, and the location of face partition cannot be accurately located. In view of the above problems, it is very necessary to design a skin problem diagnosis method for face segmentation. [0003] Existing patent does not relate to the method about s...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/34G06K9/62G16H50/30
CPCG16H50/30G06V40/172G06V10/267G06F18/214
Inventor 陈家骊刘可淳唐骢陈彦彪
Owner 广州纳丽生物科技有限公司
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