  Fundus image captures rear of an eye, and which has been studied for the
diseases identification, classification, segmentation, generation, and
biological traits association using handcrafted, conventional, and deep
learning methods. In biological traits estimation, most of the studies have
been carried out for the age prediction and gender classification with
convincing results. However, the current study utilizes the cutting-edge deep
learning (DL) algorithms to estimate biological traits in terms of age and
gender together with associating traits to retinal visuals. For the traits
association, our study embeds aging as the label information into the proposed
DL model to learn knowledge about the effected regions with aging. Our proposed
DL models, named FAG-Net and FGC-Net, correspondingly estimate biological
traits (age and gender) and generates fundus images. FAG-Net can generate
multiple variants of an input fundus image given a list of ages as conditions.
Our study analyzes fundus images and their corresponding association with
biological traits, and predicts of possible spreading of ocular disease on
fundus images given age as condition to the generative model. Our proposed
models outperform the randomly selected state of-the-art DL models.
