A Synthetic Feature Skull Descriptor for 3D Skull Similarity Measurement

Dan Zhang, Kang Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)


3D skull similarity measurement is a challenging and meaningful task in the fields of archaeology, forensic science, and anthropology. However, it is difficult to correctly and directly measure the similarity between 3D skulls which are geometric models with multiple border holes and complex topologies. In this paper, based on the synthetic feature method, we propose a novel 3D skull descriptor, synthetic wave kernel distance distribution (SWKDD) constructed by the laplace-beltrami operator. By defining SWKDD, we obtain a concise global skull representation method and transform the complex 3D skull similarity measurement into a simple 1D vector similarity measurement. First, we give the definition and calculation of SWKDD and analyse its properties. Second, we represent a framework for 3D skull similarity measurement using the SWKDD of 3D skulls and details of the calculation steps involved. Finally, we validate the effectiveness of our proposed method by calculating the similarity measurement of 3D skulls based on the real craniofacial database.

Original languageEnglish
Article number8083504
JournalMathematical Problems in Engineering
Publication statusPublished - 2019
Externally publishedYes

ASJC Scopus subject areas

  • General Mathematics
  • General Engineering


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