A Craniofacial Restoration Method Based on Deep Generative Adversarial Network

Abstract

The invention discloses a craniofacial restoration method based on a deep generative confrontation network, which includes the following steps: S1, obtaining training samples; and obtaining condition information of objects to be restored; S2, constructing a generative confrontation network, and obtaining a cost function; The three-dimensional skull information of the object to be restored is obtained to obtain the two-dimensional skull data; S3, the data in the training sample is used as the input of the generative confrontation network, and the training of the generative confrontation network is performed to optimize the cost function, and the training of the generative confrontation network is completed; S4, the data to be restored The two-dimensional skull data and conditional information of the subject are used as the input of the trained generative confrontation network to obtain the restored craniofacial information. This method is based on the generative confrontation network, and can perform high-precision craniofacial restoration according to the skull data and condition information of the object to be restored, which is helpful for the facial recognition of victims in criminal cases, the restoration of ancient faces in archaeology, and the restoration of medical facial features. Surgical outcome prediction, etc.

Description

technical field [0001] The invention relates to the field of craniofacial restoration, in particular to a craniofacial restoration method based on a deep confrontation network. Background technique [0002] Based on the skull samples to restore a close to the real craniofacial, for the facial recognition of victims in criminal cases, the restoration of ancient faces in archaeology, the prediction of the effect of medical facial surgery, and the animation production close to real human faces, etc. has a very important role. Because the structure of the human skull is very complex, the inside of the skull contains the brain, blood vessels and nerves, and the outside is covered with multiple layers of soft tissue structure. What is even more difficult is that due to differences in race, fat and thinness, the thickness of each layer of soft tissue is quite different, which makes the facial structure complex and changeable. [0003] Most early craniofacial reconstruction work w...

AI Technical Summary

Problems solved by technology

[0005] In view of the above-mentioned deficiencies in the prior art, a craniofacial restoration method based on a deep generative confrontation network provided by the present invention solves the problem of poor restoration effect of the existing craniofacial restoration methods

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