Current Result Document :
| 한글제목(Korean Title) |
젠더보존에 기반한 얼굴 합성 모델 탐구 |
| 영문제목(English Title) |
Exploring the Aged Face Synthesize Model Based on Gender Preservation |
| 저자(Author) |
이소려
이효종
Suli Li
Hyo Jong Lee
|
| 원문수록처(Citation) |
VOL 29 NO. 02 PP. 0653 ~ 0655 (2022. 11) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Face aging aims to synthesize future face images by reflecting the age factor on given faces. In recent years, deep learning-based approaches have made outstanding progress in simulating the aging process of the human face. However, generating accurate and high-quality aging faces is still intrinsically difficult. We propose a new method that incorporates gender information into the model, which achieves comparable and stable performance. Experimental results demonstrate that our method can preserve the identity well and generate diverse aged faces. |
| 키워드(Keyword) |
convolutional neural networks
generative adversarial networks
Face aging
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| 파일첨부 |
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