{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T07:35:40Z","timestamp":1753601740608,"version":"3.40.5"},"reference-count":30,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T00:00:00Z","timestamp":1623024000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Security and Communication Networks"],"published-print":{"date-parts":[[2021,6,7]]},"abstract":"<jats:p>In this study, to achieve the possibility of predicting face by skull automatically, we propose a craniofacial reconstruction method based on the end-to-end deep convolutional neural network. Three-dimensional volume data are obtained from 1447 head CT scans of Chinese people of different ages. The facial and skull surface data are projected onto two-dimensional space to generate a two-dimensional elevation map, and then, use the deep convolution neural network to realize the prediction of skull to face shape in two-dimensional space. The encoder and decoder are composed of first feature extraction through the encoder and then as the input of the decoder to generate the craniofacial restoration image. In order to accurately describe the features of different scales, we adopt an U-shaped codec structure with cross-layer connections. Therefore, the output features are decomposed with the features of the corresponding scales in the encoding stage to achieve the integration of different scales while restoring the feature scales in the compression and decoding stage. Meanwhile, the U-net structures help to avoid the problem of loss of detail features in the downsampling process. We use supervised learning to obtain the prediction model from skull to facial elevation map. Back-projection operation is performed afterwards to generate facial surface data in 3D space. Experiments show that the proposed method in this study can effectively achieve craniofacial reconstruction, and for most part of the face, restoration error is controlled within 2\u2009mm.<\/jats:p>","DOI":"10.1155\/2021\/9987792","type":"journal-article","created":{"date-parts":[[2021,6,8]],"date-time":"2021-06-08T19:38:38Z","timestamp":1623181118000},"page":"1-9","source":"Crossref","is-referenced-by-count":1,"title":["Craniofacial Reconstruction via Face Elevation Map Estimation Based on the Deep Convolution Neutral Network"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7431-7957","authenticated-orcid":true,"given":"Yining","family":"Hu","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China"},{"name":"Jiangsu Provincial Key Laboratory of Computer Network Technology, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhe","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueli","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7763-9492","authenticated-orcid":true,"given":"Lizhe","family":"Xie","sequence":"additional","affiliation":[{"name":"Institute of Stomatology, Nanjing Medical University, Nanjing 210029, China"},{"name":"Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing 210029, China"},{"name":"Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing 210029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China"},{"name":"Jiangsu Provincial Key Laboratory of Computer Network Technology, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/s0379-0738(99)00026-2"},{"volume-title":"Facial Soft Tissue Thicknesses Prediction Using Anthropometric Distances","year":"2011","author":"Q. H. Dinh","key":"2"},{"issue":"5","key":"3","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1111\/j.1556-4029.2012.02075.x","article-title":"Anatomical placement of the human eyeball in the orbit--validation using CT scans of living adults and prediction for facial approximation","volume":"57","author":"P. Guyomarc\u2019H","year":"2012","journal-title":"Journal of Forensic Sciences"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1520\/jfs14176j"},{"volume-title":"Facial Reconstruction Using Volumetric Data","year":"2001","author":"M. W. Jones","key":"5"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1520\/jfs14175j"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/s0379-0738(98)00066-8"},{"article-title":"Statistical skull models from 3D X-ray images","year":"2006","author":"M. Berar","key":"8"},{"key":"9","article-title":"3D semi-landmarks based statistical face reconstruction","volume":"14","author":"M. Desvignes","year":"2006","journal-title":"Journal of Computing & Information Technology"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1016\/j.forsciint.2006.02.035"},{"article-title":"Statistically deformable face models for cranio-facial reconstruction","author":"P. Claes","key":"11","doi-asserted-by":"crossref","DOI":"10.1109\/ISPA.2005.195436"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1016\/j.forsciint.2010.03.009"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-03798-6_24"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-012-1005-4"},{"article-title":"Stochastic backpropagation and approximate inference in deep generative models","year":"2014","author":"D. J. Rezende","key":"15"},{"article-title":"Auto-encoding variational bayes","year":"2014","author":"D. P. Kingma","key":"16"},{"key":"17","first-page":"2672","article-title":"Generative adversarial networks","volume":"3","author":"I. J. Goodfellow","year":"2014","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"Tutorial on variational autoencoders","year":"2016","author":"C. Doersch","key":"18"},{"article-title":"Wasserstein GAN","year":"2017","author":"M. Arjovsky","key":"19"},{"key":"20","first-page":"2672","article-title":"Conditional generative adversarial nets","volume":"6","author":"M. Mirza","year":"2014","journal-title":"Computer Science"},{"author":"P. Isola","key":"21","article-title":"Image-to-Image translation with conditional adversarial networks"},{"article-title":"BEGAN: boundary equilibrium generative adversarial networks","year":"2017","author":"D. Berthelot","key":"22"},{"author":"X. Li","key":"23","article-title":"Voxelized facial reconstruction using deep neural network"},{"article-title":"Sparse representation-based face object generative via deep adversarial network","author":"Y. Yuan","key":"24","doi-asserted-by":"crossref","DOI":"10.1109\/ICDH.2018.00019"},{"article-title":"Superimposition-guided facial reconstruction from skull","year":"2018","author":"C. Liu","key":"25"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1016\/j.forsciint.2009.06.017"},{"volume-title":"U-net: Convolutional Networks for Biomedical Image Segmentation","year":"2015","author":"O. Ronneberger","key":"27"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.23915\/distill.00003"},{"key":"29","article-title":"Group normalization","volume":"14","author":"Y. Wu","year":"2018","journal-title":"International Journal of Computer Vision"},{"key":"30","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"41","author":"Z. Wang","year":"2004","journal-title":"IEEE Transactions on Image Processing"}],"container-title":["Security and Communication Networks"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2021\/9987792.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2021\/9987792.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2021\/9987792.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,8]],"date-time":"2021-06-08T19:38:47Z","timestamp":1623181127000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/scn\/2021\/9987792\/"}},"subtitle":[],"editor":[{"given":"Beijing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,6,7]]},"references-count":30,"alternative-id":["9987792","9987792"],"URL":"https:\/\/doi.org\/10.1155\/2021\/9987792","relation":{},"ISSN":["1939-0122","1939-0114"],"issn-type":[{"type":"electronic","value":"1939-0122"},{"type":"print","value":"1939-0114"}],"subject":[],"published":{"date-parts":[[2021,6,7]]}}}