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LAGAN: Landmark Aided Text to Face Sketch Generation

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Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13537))

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Abstract

Face sketch is a concise representation of the human face, and it has a variety of applications in criminal investigation, biometrics, and social entertainment. It is well known that facial attribute is an underlying representation of the facial description. However, generating vivid face sketches, especially sketches with rich details, from given facial attributes text is still a challenging task as the text information is limited. Existing work synthetic face sketch is not realistic, especially the facial areas are not natural enough, even distorted. We aim to relieve the situation by introducing face prior knowledge, such as landmarks. This paper proposes a method, called LAGAN, that Landmark Aided Text to Face Sketch Generation. Specifically, we design a novel scale translation-invariant similarity loss based on the facial landmarks. It can measure the mutual similarity between real sketch and synthetic sketch and also measure the self similarity based on the symmetry of face attributes. Further to counter data deficiency, we construct a novel facial attribute text to sketch dataset called TextCUFSF with CUFSF face sketch dataset. Each sketch has 4 manual annotations. Qualitative and quantitative experiments demonstrate the effectiveness of our proposed method for sketch synthesis with attribute text. The code and data are available: https://github.com/chaowentao/LAGAN.

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Acknowledgement

This work was supported by the National Key Research and Development Program of China under grant No. 2019YFC1521104, Natural Science Foundation of China (61772050, 62172247).

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Authors and Affiliations

  1. Beijing Normal University, Beijing, China

    Wentao Chao, Liang Chang, Fangfang Xi & Fuqing Duan

Authors
  1. Wentao Chao
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  2. Liang Chang
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  3. Fangfang Xi
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  4. Fuqing Duan
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Corresponding author

Correspondence to Liang Chang .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi'an, China

    Junwei Han

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

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Chao, W., Chang, L., Xi, F., Duan, F. (2022). LAGAN: Landmark Aided Text to Face Sketch Generation. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_12

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  • DOI: https://doi.org/10.1007/978-3-031-18916-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18915-9

  • Online ISBN: 978-3-031-18916-6

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