Paper Abstract and Keywords |
Presentation |
2022-12-07 10:50
[Short Paper]
3D Facial Recognition for Genetic Studies based on PointNet++ Kazuma Okada, Takuma Terada, Jiaqing Liu (Ritsumeikan Univ.), Tomoko Tateyama (Fujita Health University), Ryosuke Kimura (Ryukyu Univ.), Yen Wei Chen (Ritsumeikan Univ.) |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Recently, the development of genetic research has found a relationship between human face shape and genes. By analyzing facial shape, it is possible to elucidate which genetic information influences facial shape. However, 3D data is difficult to analyze because of its high dimensionality. Therefore, in this study, a point cloud deep learning approach is used to classify face shapes. Accurate classification can be achieved by using PointNet++, which can extract important local features in face shapes. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Genetic Study / 3D Face Shape / Deep Learning / / / / / |
Reference Info. |
ITE Tech. Rep., vol. 46, no. 39, ME2022-88, pp. 9-11, Dec. 2022. |
Paper # |
ME2022-88 |
Date of Issue |
2022-11-30 (ME, SIP) |
ISSN |
Print edition: ISSN 1342-6893 Online edition: ISSN 2424-1970 |
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