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Landmark-Guided Conditional GANs for Face Aging

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Image Analysis and Processing – ICIAP 2022 (ICIAP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13231))

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Abstract

Face aging, which alters a person’s facial photo to the appearance at a different age, is a popular topic in multimedia applications. Recently, conditional Generative Adversarial Networks (cGANs) have achieved visually impressive progress in this area. However, generating a convincing aging appearance while preserving the person’s identity is still a challenging task. In this paper, we propose a novel Landmark-Guided cGAN (LGcGAN), which not only synthesizes texture changes related to aging, but also alters facial structures accordingly. We adapt a built-in attention mechanism to emphasize the most discriminative regions relevant to aging and minimize changes that affect personal identity and background. Conditioned with age vectors, the primal cGAN in our symmetric network converts input faces to target ages, and the dual cGAN inverts the previous task, which feeds synthesized target faces back to the original input age scope for enhancing age consistency. Both qualitative and quantitative experiments show that our method can generate appealing results in terms of image quality, personal identity, and age accuracy.

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Authors and Affiliations

  1. Memorial University of Newfoundland, St. John’s, NL, A1C 5S7, Canada

    Xin Huang

  2. University of Guelph, Guelph, ON, N1G 2W1, Canada

    Minglun Gong

Authors
  1. Xin Huang
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  2. Minglun Gong
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Corresponding author

Correspondence to Minglun Gong .

Editor information

Editors and Affiliations

  1. Boston University, Boston, MA, USA

    Stan Sclaroff

  2. National Research Council, Lecce, Italy

    Cosimo Distante

  3. National Research Council, Lecce, Italy

    Marco Leo

  4. University of Catania, Catania, Italy

    Giovanni M. Farinella

  5. Technische Universität München, Garching, Germany

    Federico Tombari

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Huang, X., Gong, M. (2022). Landmark-Guided Conditional GANs for Face Aging. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13231. Springer, Cham. https://doi.org/10.1007/978-3-031-06427-2_23

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  • DOI: https://doi.org/10.1007/978-3-031-06427-2_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06426-5

  • Online ISBN: 978-3-031-06427-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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