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Generative Networks

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Handbook of Face Recognition

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Abstract

Synthesizing photorealistic human faces is an appealing yet challenging task. Before the advent of deep learning, researchers used predefined 3D face models to design generative models for facial images. However, the abstraction and distortion of predefined models hinder the realism of the generated faces. With the development of deep learning, a large number of generative models have been proposed, especially in the field of face image generation.

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Authors and Affiliations

  1. S-Lab, Nanyang Technological University, Singapore, Singapore

    Ziwei Liu, Shuai Yang, Yuming Jiang & Ziqi Huang

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  1. Ziwei Liu
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  2. Shuai Yang
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  3. Yuming Jiang
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  4. Ziqi Huang
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Correspondence to Ziwei Liu .

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Editors and Affiliations

  1. Westlake University, Hangzhou, China

    Stan Z. Li

  2. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Anil K. Jain

  3. Department of Computing, Imperial College London, London, UK

    Jiankang Deng

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Liu, Z., Yang, S., Jiang, Y., Huang, Z. (2024). Generative Networks. In: Li, S.Z., Jain, A.K., Deng, J. (eds) Handbook of Face Recognition. Springer, Cham. https://doi.org/10.1007/978-3-031-43567-6_3

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