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Skull similarity comparison based on SPCA

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Abstract

As an important biological feature of the human, the skull plays an active role in assisting criminal investigation, victim identification, etc. This paper proposes a method based on Sparse Principal Component Analysis (SPCA) for comparison of skull similarity. Compared with Principal Component Analysis (PCA), SPCA can not only effectively reduce the data dimension,but also produce sparse principal components which are easy to explain. Each principal component of PCA is a linear combination of all original variables. It’s difficulty in explaining the corresponding relationship between principal components and features. SPCA makes the loadings sparse, and thus highlights the main part of the principal component, which can solve the problem of PCA that has difficulty in explaining the result. The experimental results show that the dimensionality reduction data by SPCA is superior to PCA in the aspects of complexity, discrimination, stability, interpretability, and similarity evaluation. These indicate that the comparison of skull similarity based on SPCA is accurate and stable, which can provide an effective direction for improving the accuracy of craniofacial reconstruction and obtaining accurate reconstruction results.

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Acknowledgements

The authors gratefully appreciate the anonymous reviewers for all of their helpful comments. We also acknowledge the support of Xianyang Hospital for providing CT images.This work was supported by the National Natural Science Foundation of China under Grant Nos. 61702293, 61772294, 61572078, 61902203, 11572066 and 11602047, China Postdoctoral Science Foundation No.2017M622137, Key Research and Development Plan - Major Scientific and Technological Innovation Projects of ShanDong Province No.2019JZZY020101, the Open Research Fund of the Ministry of Education Engineering Research Center of Virtual Reality Application of China under Grant No.MEOBNUEV RA201601.

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Authors and Affiliations

  1. School of Data Science and Software Engineering, Qingdao University, Qingdao, 266071, China

    Xin Zheng, Junli Zhao & Zhihan Lv

  2. College of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China

    Fuqing Duan

  3. College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China

    Zhenkuan Pan

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  1. Xin Zheng
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  2. Junli Zhao
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  4. Fuqing Duan
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  5. Zhenkuan Pan
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Correspondence to Junli Zhao.

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Zheng, X., Zhao, J., Lv, Z. et al. Skull similarity comparison based on SPCA. Multimed Tools Appl 79, 22423–22446 (2020). https://doi.org/10.1007/s11042-020-08937-z

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  • DOI: https://doi.org/10.1007/s11042-020-08937-z

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