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A system for quantifying facial symmetry from 3D contour maps based on transfer learning and fast R-CNN.

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Title: A system for quantifying facial symmetry from 3D contour maps based on transfer learning and fast R-CNN.
Authors: Lin, Hsiu-Hsia1, Zhang, Tianyi2, Wang, Yu-Chieh2, Yang, Chao-Tung2,3, ctyang@thu.edu.tw, Lo, Lun-Jou4, lunjoulo@cgmh.org.tw, Liao, Chun-Hao1, Kuang, Shih-Ku1
Source: Journal of Supercomputing; Sep2022, Vol. 78 Issue 14, p15953-15973, 21p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 159003739
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: A system for quantifying facial symmetry from 3D contour maps based on transfer learning and fast R-CNN.
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=159003739
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s11227-022-04502-7
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      – Code: eng
        Text: English
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        PageCount: 21
        StartPage: 15953
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      – TitleFull: A system for quantifying facial symmetry from 3D contour maps based on transfer learning and fast R-CNN.
        Type: main
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            NameFull: Lin, Hsiu-Hsia
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            NameFull: Zhang, Tianyi
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            NameFull: Wang, Yu-Chieh
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            NameFull: Lo, Lun-Jou
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            NameFull: Liao, Chun-Hao
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            NameFull: Kuang, Shih-Ku
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          Dates:
            – D: 15
              M: 09
              Text: Sep2022
              Type: published
              Y: 2022
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            – TitleFull: Journal of Supercomputing
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