Skull appearance restoration model construction method and skull appearance restoration method and system

Abstract

The invention discloses a skull appearance restoration model construction method and a skull appearance restoration method. The model construction method comprises the steps of creating a sample dataset, wherein the sample training set comprises a plurality of skull samples and skin samples of the skull samples, and the skull samples and the skin of the skull samples are subjected to coordinate normalization and scale normalization processing; wherein the skull samples and the skin samples are two-dimensional data; and training an initial generative adversarial network by using the sample training set, with the initial generative adversarial network comprising a generator and a discriminator, the generator being an improved U-Net network, and the discriminator being a PatchGAN network. According to the restoration method, the constructed model is used for carrying out facial restoration on the unknown skull. The invention is convenient to operate, and the defect that a traditional method is insufficient in craniofacial nonlinear deformation representation is overcome; the defect that a traditional method depends on template selection is overcome; and the defect of insufficient representation of the texture and shape of a wrapper in the existing method is overcome.

Description

technical field [0001] The invention belongs to the field of three-dimensional reconstruction, and relates to a method for restoring the appearance of a skull by using a generative confrontation network. It is mainly used in criminal investigation, archaeology, forensic anthropology and other fields. Background technique [0002] In the field of biometric identification, face restoration through skull is an important research content in this field. Facial restoration has certain applications in the fields of forensic anthropology, criminal investigation, and identification of unknown skulls, providing an important basis for future related research. [0003] The early expression and description of the essential relationship between craniofacial morphology relied on the subjective understanding of craniofacial morphology by anthropological experts, and there was no exact definition. Therefore, early craniofacial restoration relied on experts' manual restoration, repeatability...

AI Technical Summary

Problems solved by technology

In summary, the knowledge analysis model method lacks sufficient explicit knowledge, and it is difficult to effectively represent the complexity and diversity of craniofacial morphological changes; the statistical learning model has insufficient representation ability, and the modeling methods all use linear methods such as PCA and PLSR , is insufficient to represent the nonlinear deformation of the craniofacial

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