Improved STGAN for Facial Attribute Editing by Utilizing Mask Information (얼굴 속성 편집을 위한 마스크 정보를 활용한 개선된 STGAN)

Hyeon Seok Yang (양현석); HAN，BOKGYU (한복규); Young Shik Moon (문영식)

doi:10.9708/jksci.2020.25.05.001

Improved STGAN for Facial Attribute Editing by Utilizing Mask Information

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2020, 25(5), pp.1-9
DOI : 10.9708/jksci.2020.25.05.001
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : April 20, 2020
Accepted : May 14, 2020
Published : May 29, 2020

Hyeon Seok Yang ¹, HAN，BOKGYU ¹, Young Shik Moon ¹

¹한양대학교

Accredited

ABSTRACT

In this paper, we propose a model that performs more natural facial attribute editing by utilizing mask information in the hair and hat region. STGAN, one of state-of-the-art research of facial attribute editing, has shown results of naturally editing multiple facial attributes. However, editing hair-related attributes can produce unnatural results. The key idea of the proposed method is to additionally utilize information on the face regions that was lacking in the existing model. To do this, we apply three ideas. First, hair information is supplemented by adding hair ratio attributes through masks. Second, unnecessary changes in the image are suppressed by adding cycle consistency loss. Third, a hat segmentation network is added to prevent hat region distortion. Through qualitative evaluation, the effectiveness of the proposed method is evaluated and analyzed. The method proposed in the experimental results generated hair and face regions more naturally and successfully prevented the distortion of the hat region.

KEYWORDS

Facial attribute editing, GAN, Deep learning, Mask, STGAN

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* References for papers published after 2023 are currently being built.

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KJCKorea
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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Improved STGAN for Facial Attribute Editing by Utilizing Mask Information

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Improved STGAN for Facial Attribute Editing by Utilizing Mask Information

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.