| Autor: |
Daiki YAMADA, Toshinobu HARADA, Akira YAMADA, SHAH, Nikhil, CHWA, Emily, ALLISON, Sophia |
| Zdroj: |
Transactions of Japan Society of Kansei Engineering; 2024, Vol. 23 Issue 2, p131-139, 9p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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