Several techniques are available for face age progression still identity preservation as well age estimation accuracy are big challenges and need attention. So, the proposed work is focused on these key issues using Generative Adversarial Networks (GANs). To produce a realistic appearance with an enhanced vision of face image, a fusion-based Generative Adversarial Network approach is used. GAN has a generator and a discriminator network. The generator produces fake images which are further differentiated by discriminator whether the image is real or fake. 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. Simulation results on five face datasets, namely IMDB-WIKI, CACD and UTKFace, FGNET, Celeb A are evaluated. 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. 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.

有几种技术可用于保持面部年龄的进展，同时保持身份，以及年龄估计的准确性是巨大的挑战，需要引起注意。因此，拟议的工作集中在使用对抗性生成网络（GAN）的这些关键问题上。为了产生具有增强的面部图像视觉效果的逼真的外观，使用了基于融合的“生成对抗网络”方法。GAN有一个生成器和一个鉴别器网络。生成器生成伪造的图像，通过辨别器进一步区分图像是真实的还是伪造的。最初，Cycle-Generative Adversarial Network（CycleGAN）实现了面部年龄增长，进一步的增强型超分辨率Generative Adversarial Network（ESRGAN）自动增强了老年面部图像以提高可见度。五个人脸数据集（IMDB-WIKI）的仿真结果，对CACD和UTKFace，FGNET，Celeb A进行了评估。与以前的技术相比，使用带有参数置信度分数和年龄估计值的定量Face ++研究工具包可以观察到所建议的工作功效。可以看出，所提出的工作精确地产生了老龄化的面部，其错误率为0.001％，在数据集上的置信度得分为95.13至95.39。