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A Transfer Learning for Line-Based Portrait Sketch

Author

Listed:
  • Hyungbum Kim

    (Department of Computer Science, Sangmyung University, Seoul 03016, Korea)

  • Junyoung Oh

    (Department of Computer Science, Sangmyung University, Seoul 03016, Korea)

  • Heekyung Yang

    (Division of SW Convergence, Sangmyung University, Seoul 03016, Korea)

Abstract

This paper presents a transfer learning-based framework that produces line-based portrait sketch images from portraits. The proposed framework produces sketch images using a GAN architecture, which is trained through a pseudo-sketch image dataset. The pseudo-sketch image dataset is constructed from a single artist-created portrait sketch using a style transfer model with a series of postprocessing schemes. The proposed framework successfully produces portrait sketch images for portraits of various poses, expressions and illuminations. The excellence of the proposed model is proved by comparing the produced results with those from the existing works.

Suggested Citation

  • Hyungbum Kim & Junyoung Oh & Heekyung Yang, 2022. "A Transfer Learning for Line-Based Portrait Sketch," Mathematics, MDPI, vol. 10(20), pages 1-14, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3869-:d:946330
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