書誌事項
- タイトル別名
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- Feature Extraction using Deep Learning and Analyses of Curvature on Facial Shapes across Two Races and between Males and Females
抄録
<p>In plastic surgery for facial reconstruction and gender conformity, the aspect of appearance of a natural-looking male/ female face is an important factor in the perfection of the surgery. However, there is a problem that the perfection of the postoperative facial shapes after surgery is greatly influenced by the skill of each plastic surgeon. Therefore, it is useful to verify the male/female areas of each patient’s face in order to create an appropriate shape for each patient. In this study, we generated 100 cross-sectional images per person from 3D models of male and female faces, and trained a convolutional neural network (CNN) using gender and race as the classification criteria The trained CNN was then used to visualize the acquired facial features using Grad-CAM and analyze the feature curves. The results revealed that the characteristics of the curves in specific facial regions represent the gender and racial traits.</p>
収録刊行物
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- 日本感性工学会論文誌
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日本感性工学会論文誌 23 (2), 131-139, 2024
日本感性工学会
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詳細情報 詳細情報について
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- CRID
- 1390018506586593920
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- ISSN
- 18845258
- 18840833
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- Crossref
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- 抄録ライセンスフラグ
- 使用不可