Cargando…
Prediction of face age progression with generative adversarial networks
Face age progression, goals to alter the individual’s face from a given face image to predict the future appearance of that image. In today’s world that demands more security and a touchless unique identification system, face aging attains tremendous attention. The existing face age progression appr...
Autores principales: | Sharma, Neha, Sharma, Reecha, Jindal, Neeru |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8397612/ https://www.ncbi.nlm.nih.gov/pubmed/34483708 http://dx.doi.org/10.1007/s11042-021-11252-w |
Ejemplares similares
-
Comparative analysis of CycleGAN and AttentionGAN on face aging application
por: Sharma, Neha, et al.
Publicado: (2022) -
Adversarial Patch Attacks on Deep-Learning-Based Face Recognition Systems Using Generative Adversarial Networks
por: Hwang, Ren-Hung, et al.
Publicado: (2023) -
Presentation Attack Face Image Generation Based on a Deep Generative Adversarial Network
por: Nguyen, Dat Tien, et al.
Publicado: (2020) -
Conditional Progressive Generative Adversarial Network for satellite image generation
por: Cardoso, Renato, et al.
Publicado: (2022) -
PlasticGAN: Holistic generative adversarial network on face plastic and aesthetic surgery
por: Chandaliya, Praveen Kumar, et al.
Publicado: (2022)