Face aging on SiGan

Jing Yang; Caizeng Ye; Bei Han; Jilin Qin; Lei Peng

doi:10.1117/12.2671372

16 March 2023 Face aging on SiGan

Jing Yang, Caizeng Ye, Bei Han, Jilin Qin, Lei Peng

Proceedings Volume 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022); 1259313 (2023) https://doi.org/10.1117/12.2671372
Event: 2nd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 2022, Guangzhou, China

Abstract

Cross-age image generation technology is to generate cross-age face images on the basis of the original face image. The synthetic face image can show facial details such as skin, wrinkles and hair at a certain age. The technology can be widely used in film and television, animation, public safety and other fields. Cross-age face synthesis techniques can be divided into traditional cross-age face synthesis techniques and cross-age face synthesis techniques based on generative adversarial network models. With the continuous development of GAN, the technologies based on generative adversarial network models have made more progress and advantages in the field of face synthesis. The model in this paper, based on the generation of the adversarial network model, combines the advantages of the conditional autoencoder and the StyleGAN model, and innovates in the use of the feature contrasting device, which can generate HD face images consistent with the change logic across ages, and effectively avoid the emergence of problems such as organ deformation and identity inconsistency.

Citation Download Citation

Jing Yang, Caizeng Ye, Bei Han, Jilin Qin, and Lei Peng "Face aging on SiGan", Proc. SPIE 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 1259313 (16 March 2023); https://doi.org/10.1117/12.2671372

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KEYWORDS

Deformation

Image processing

Image quality

Computer vision technology

Pattern recognition

Distortion

Education and training

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