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3D craniofacial similarity calculation and craniofacial relationships analysis based on spectral analysis method

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Abstract

The field of 3D craniofacial similarity calculation and craniofacial relationships(the relationships between the human skull and face) analysis is a challenging and meaningful task in archaeology, forensic science, and anthropology. Although anthropologists have been involved in the application and illustration of craniofacial relationships in their research, due to the complexity of the 3D skull (with multiple holes), and the facial expression changes of 3D human faces, it is difficult to accurately perform 3D craniofacial similarity calculations and theoretical validation of craniofacial relationships are difficult. To address this challenge, we propose a data-driven framework that constructs a shape feature space based on spectral analysis to describe the intrinsic structure of 3D skulls and faces. Our framework includes a shape analysis method to measure 3D craniofacial similarity and a statistical method using canonical correlation analysis to comprehensively describe the craniofacial relationships from global statistical features and individual geometric features. Based on an Asian craniofacial database, we demonstrate the effectiveness of our framework through validation results and skull identification. Most importantly, we provide two craniofacial relationships rules through theoretical validation and numerical results: R1-the human skull has a strong correlation with the face; R2-the similarity change trend of the skull is generally consistent with the corresponding face similarity. These craniofacial relationships rules can be applied to general craniofacial analysis tasks and scenarios, providing a solid theoretical basis for relevant researchers. Our research represents a significant contribution to the field of 3D craniofacial similarity calculation and craniofacial relationships analysis.

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Data Availibility Statement

The datasets generated and/or analysed during the current study are not publicly available due the craniofacial database used in this paper consumes huge economic, human and material resources during the establishment, our research team only has the right to use this data set for research, and has no public right but are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank the Natural Science Youth Foundation of Qinghai Province(No.2023-ZJ-947Q); National Natural Science Foundation of China (Grant Nos. 62102213); Independent project fund of the state key lab of the Tibetan Intelligent Information Processing and Application (Co-established by the province and the ministry)(Grant Nos.2022-SKL-014); Young and middle-aged scientific research fund of Qinghai Normal University (Grant Nos.kjqn 2021004). The authors would also like to thank the database provider the Institute of Virtual Reality and Visualization Technology, Beijing Normal University.

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Authors and Affiliations

  1. School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China

    Dan Zhang, Na Liu, Zhongke Wu & Xingce Wang

  2. School of Computer Science, Qinghai Normal University, Xining, 81017, People’s Republic of China

    Dan Zhang

  3. State Key lab of Tibetan Intelligent Information Processing and Application(Co-established by province and ministry), Xining, 81017, People’s Republic of China

    Dan Zhang

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  1. Dan Zhang
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Zhang, D., Liu, N., Wu, Z. et al. 3D craniofacial similarity calculation and craniofacial relationships analysis based on spectral analysis method. Multimed Tools Appl 83, 14063–14084 (2024). https://doi.org/10.1007/s11042-023-16048-8

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