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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...

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Autores principales: Zhao, Junli, Duan, Fuqing, Pan, Zhenkuan, Wu, Zhongke, Li, Jinhua, Deng, Qingqiong, Li, Xiaona, Zhou, Mingquan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
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.
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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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