1997 Fiscal Year Final Research Report Summary
Development of Three-Dimension computer graphic system for reconstruction of the face features from the skull.
| Project/Area Number |
07557269
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 展開研究 |
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| Research Field |
病態科学系歯学(含放射線系歯学)
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| Research Institution | KYUSHU UNIVERSITY |
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Principal Investigator |
YUASA Kenji Kyushu University, Department of Oral and Maxillofacial Radiology, Assistant Professor, 歯学部, 講師 (40136510)
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| Co-Investigator(Kenkyū-buntansha) |
MIWA Kunihiro Kyushu University, Department of Oral and Maxillofacial Radiology, Research Asso, 歯学部, 助手 (10136509)
TANAKA Takemasa Kyushu University, Department of Oral and Maxillofacial Radiology, Research Asso, 歯学部, 助手 (30163538)
YOSHIURA Kazunori Kyushu University, Department of Oral and Maxillofacial Radiology, Assistant Pro, 歯学部, 講師 (20210643)
TOKUMORI Kenji Kyushu University, Department of Oral and Maxillofacial Radiology, Research Asso, 歯学部, 助手 (40253463)
SHIBAKI Norio Seika Women's Junior College, Department of Living Science, Associate Professor, 生活科学科, 助教授 (10249617)
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| Project Period (FY) |
1995 – 1997
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| Keywords | face / eigen-faces / principal component analysis / masticator muscle |
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| Research Abstract |
At first We analyzed the relationships between the size of organs that formed face features such as the masseter muscle and the parotid gland, and characteristic of each subject such as sex, age, the size of the skull, height, weight, body mass index and number of residual molar teeth. The size of each organs were measured using CT images. We evaluated the relationships using multiple regression analysis. And the function for predict value of each organ in face was made. Their multiple correlation coefficients were 0.71 to 0.74.
We evaluated the grade of reconstruction of face that persons could identify each subject using cropped faces of each subject which were reconstructed using the eigen-face methods, which is based on a methods known as the principal component analysis. Furthermore, the facial images was classified by cluster analysis. As a results, It was consider that individual face was identified to cropped face with at least 7 eigen-faces, that is, the grade of reconstruction was 0.88 of correlation coefficients. Furthermore, face images were classified into 5 groups by cluster analysis.
We analyzed the factors in face by which persons identify individuals. As results, The most important factor was the distance between both eyes. Secondly, the width of the lips.
As above mentioned, we obtained useful results concerning with the relationships between face and the individual features (skull size, sex, age, body mass, masticator function), and the grade of reconstruction of face for identifying the individual face. However, we are developing the computer graphic software for reconstruction of the face features now.
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