High-precision face key point positioning method and system based on deep learning
The invention discloses a high-precision face key point positioning method and system based on deep learning. The positioning method comprises the following steps: S1, constructing a plurality of regional key point positioning networks; S2, through the portrait area and the key point sample data cor...
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| creator | HU NENG YANG JINJIANG DAI KANKAN LI YUNXI |
| description | The invention discloses a high-precision face key point positioning method and system based on deep learning. The positioning method comprises the following steps: S1, constructing a plurality of regional key point positioning networks; S2, through the portrait area and the key point sample data corresponding to each area, training a corresponding area key point positioning network; S3, segmentingthe to-be-processed face image into human image regions; S4, based on the processing task type of the face image, selecting portrait areas needing to be processed, and inputting the portrait areas into the corresponding key point positioning networks to obtain key points corresponding to the processing task; and S5, integrating and outputting the key points corresponding to the processing task and the face image. According to the invention, the human face is divided into a plurality of regions for independently positioning the key points, when one or more parts are shielded, the accuracy andstability of the key point |
| format | Patent |
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The positioning method comprises the following steps: S1, constructing a plurality of regional key point positioning networks; S2, through the portrait area and the key point sample data corresponding to each area, training a corresponding area key point positioning network; S3, segmentingthe to-be-processed face image into human image regions; S4, based on the processing task type of the face image, selecting portrait areas needing to be processed, and inputting the portrait areas into the corresponding key point positioning networks to obtain key points corresponding to the processing task; and S5, integrating and outputting the key points corresponding to the processing task and the face image. 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The positioning method comprises the following steps: S1, constructing a plurality of regional key point positioning networks; S2, through the portrait area and the key point sample data corresponding to each area, training a corresponding area key point positioning network; S3, segmentingthe to-be-processed face image into human image regions; S4, based on the processing task type of the face image, selecting portrait areas needing to be processed, and inputting the portrait areas into the corresponding key point positioning networks to obtain key points corresponding to the processing task; and S5, integrating and outputting the key points corresponding to the processing task and the face image. 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The positioning method comprises the following steps: S1, constructing a plurality of regional key point positioning networks; S2, through the portrait area and the key point sample data corresponding to each area, training a corresponding area key point positioning network; S3, segmentingthe to-be-processed face image into human image regions; S4, based on the processing task type of the face image, selecting portrait areas needing to be processed, and inputting the portrait areas into the corresponding key point positioning networks to obtain key points corresponding to the processing task; and S5, integrating and outputting the key points corresponding to the processing task and the face image. According to the invention, the human face is divided into a plurality of regions for independently positioning the key points, when one or more parts are shielded, the accuracy andstability of the key point</abstract><oa>free_for_read</oa></addata></record> |
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| language | chi ; eng |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
| title | High-precision face key point positioning method and system based on deep learning |
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