Craniofacial similarity analysis through sparse principal component analysis

Title

Craniofacial similarity analysis through sparse principal component analysis

Creator(s)

Zhao, Junli

Duan, Fuqing

Pan, Zhenkuan

Wu, Zhongke

Li, Jinhua

Deng, Qingqiong

Li, Xiaona

Zhou, Mingquan

Date

2017

Is Part Of

Plos one

Volume

12

Issue

6

Pages

e0179671

Language

eng

ISSN

1932-6203

DOI

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0179671

Zotero BiDiAr

https://www.zotero.org/groups/5293298/bidiar/items/CNEPFX5Jitem-list

Abstract

The computer-aided craniofacial reconstruction (CFR) technique has been widely used in the fields of criminal investigation, archaeology, anthropology and cosmetic surgery. The evaluation of craniofacial reconstruction results is important for improving the effect of craniofacial reconstruction. Here, we used the sparse principal component analysis (SPCA) method to evaluate the similarity between two sets of craniofacial data. Compared with principal component analysis (PCA), SPCA can effectively reduce the dimensionality and simultaneously produce sparse principal components with sparse loadings, thus making it easy to explain the results. The experimental results indicated that the evaluation results of PCA and SPCA are consistent to a large extent. To compare the inconsistent results, we performed a subjective test, which indicated that the result of SPCA is superior to that of PCA. Most importantly, SPCA can not only compare the similarity of two craniofacial datasets but also locate regions of high similarity, which is important for improving the craniofacial reconstruction effect. In addition, the areas or features that are important for craniofacial similarity measurements can be determined from a large amount of data. We conclude that the craniofacial contour is the most important factor in craniofacial similarity evaluation. This conclusion is consistent with the conclusions of psychological experiments on face recognition and our subjective test. The results may provide important guidance for three- or two-dimensional face similarity evaluation, analysis and face recognition.