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Craniofacial similarity analysis through sparse principal component analysis
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. Her...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5480975/ https://www.ncbi.nlm.nih.gov/pubmed/28640836 http://dx.doi.org/10.1371/journal.pone.0179671 |
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author | Zhao, Junli Duan, Fuqing Pan, Zhenkuan Wu, Zhongke Li, Jinhua Deng, Qingqiong Li, Xiaona Zhou, Mingquan |
author_facet | Zhao, Junli Duan, Fuqing Pan, Zhenkuan Wu, Zhongke Li, Jinhua Deng, Qingqiong Li, Xiaona Zhou, Mingquan |
author_sort | Zhao, Junli |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5480975 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-54809752017-07-05 Craniofacial similarity analysis through sparse principal component analysis Zhao, Junli Duan, Fuqing Pan, Zhenkuan Wu, Zhongke Li, Jinhua Deng, Qingqiong Li, Xiaona Zhou, Mingquan PLoS One Research Article 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. Public Library of Science 2017-06-22 /pmc/articles/PMC5480975/ /pubmed/28640836 http://dx.doi.org/10.1371/journal.pone.0179671 Text en © 2017 Zhao et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zhao, Junli Duan, Fuqing Pan, Zhenkuan Wu, Zhongke Li, Jinhua Deng, Qingqiong Li, Xiaona Zhou, Mingquan Craniofacial similarity analysis through sparse principal component analysis |
title | Craniofacial similarity analysis through sparse principal component analysis |
title_full | Craniofacial similarity analysis through sparse principal component analysis |
title_fullStr | Craniofacial similarity analysis through sparse principal component analysis |
title_full_unstemmed | Craniofacial similarity analysis through sparse principal component analysis |
title_short | Craniofacial similarity analysis through sparse principal component analysis |
title_sort | craniofacial similarity analysis through sparse principal component analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5480975/ https://www.ncbi.nlm.nih.gov/pubmed/28640836 http://dx.doi.org/10.1371/journal.pone.0179671 |
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