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A3GAN: An attribute-aware attentive generative adversarial network for face aging. In Computer vision and pattern recognition. Face aging with conditional generative adversarial networks. In IEEE.Īntipov, G., Baccouche, M., & Dugelay, J. Learning face age progression: A Pyramid Architecture of GANs. Yang, H., Huang, D., Wang, Y., & Jain, A. Child face age-progression via deep feature aging. Modeling of facial aging and kinship: A survey. Georgopoulos, M., Panagakis, Y., & Pantic, M., (2018). international conference visualization, imaging, and image processing (pp. Soft biometric traits for personal recognition systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(11), 1955–1976. Age synthesis and estimation via faces: A survey. The boy who didn’t know he was abducted for thirteen years.įu, Y., Guo, G., & Huang, T. It is observed that the proposed work produces the aged face precisely with an error rate of 0.001%, with a a confidence score 95.13 to 95.39 on datasets. The proposed work efficacy is observed in comparison to previous techniques using a quantitative Face ++ research toolkit with parameters confidence score number and age estimation value. Simulation results on five face datasets, namely IMDB-WIKI, CACD and UTKFace, FGNET, Celeb A are evaluated. Initially, Cycle-Generative Adversarial Network (CycleGAN) achieves the face age progression, further Enhanced Super-resolution Generative Adversarial Network (ESRGAN) automatically enhance the aged face image to improve the visibility.
The generator produces fake images which are further differentiated by discriminator whether the image is real or fake. GAN has a generator and a discriminator network. To produce a realistic appearance with an enhanced vision of face image, a fusion-based Generative Adversarial Network approach is used. So, the proposed work is focused on these key issues using Generative Adversarial Networks (GANs). Several techniques are available for face age progression still identity preservation as well age estimation accuracy are big challenges and need attention.